Interspeech
Collection
Accepted papers for Interspeech (Annual Conference of the International Speech Communication Association), one dataset per year. • 13 items • Updated
paper_id stringlengths 15 34 | title stringlengths 25 167 | authors listlengths 1 22 | isca_url stringlengths 66 85 | pdf_url stringlengths 65 84 | doi stringlengths 28 30 | pages stringlengths 3 9 | bibtex large_stringlengths 266 763 | abstract large_stringlengths 427 1.57k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|
wesolek24_interspeech | The influence of L2 accent strength and different error types on personality trait ratings | [
"Sarah Wesolek",
"Piotr Gulgowski",
"Joanna Blaszczak",
"Marzena Zygis"
] | https://www.isca-archive.org/interspeech_2024/wesolek24_interspeech.html | https://www.isca-archive.org/interspeech_2024/wesolek24_interspeech.pdf | 10.21437/Interspeech.2024-1669 | 2-6 | @inproceedings{wesolek24_interspeech,
title = {{The influence of L2 accent strength and different error types on personality trait ratings}},
author = {Sarah Wesolek and Piotr Gulgowski and Joanna Blaszczak and Marzena Zygis},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {2--6},
... | Accents can have a detrimental impact on interpersonal evaluations. However, the influence of specific language errors remains less understood. The present study tests how accent strength (constructed as a graded factor obtained through ratings) impacts evaluations of speakersâ personality traits (warmth, competence)... | null | null |
chi24_interspeech | Characterizing code-switching: Applying Linguistic Principles for Metric Assessment and Development | [
"Jie Chi",
"Electra Wallington",
"Peter Bell"
] | https://www.isca-archive.org/interspeech_2024/chi24_interspeech.html | https://www.isca-archive.org/interspeech_2024/chi24_interspeech.pdf | 10.21437/Interspeech.2024-551 | 7-11 | @inproceedings{chi24_interspeech,
title = {{Characterizing code-switching: Applying Linguistic Principles for Metric Assessment and Development}},
author = {Jie Chi and Electra Wallington and Peter Bell},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {7--11},
doi = {10.21437... | With handling code-switching becoming an increasingly important topic in speech technology, driven by the expansion of low-resource and multilingual methodologies, it is vital that we recognize the diversity of code-switching as a phenomenon. We propose a framework that leverages linguistic findings as makeshift ground... | null | null |
xue24_interspeech | Towards a better understanding of receptive multilingualism: listening conditions and priming effects | [
"Wei Xue",
"Ivan Yuen",
"Bernd Möbius"
] | https://www.isca-archive.org/interspeech_2024/xue24_interspeech.html | https://www.isca-archive.org/interspeech_2024/xue24_interspeech.pdf | 10.21437/Interspeech.2024-418 | 12-16 | @inproceedings{xue24_interspeech,
title = {{Towards a better understanding of receptive multilingualism: listening conditions and priming effects}},
author = {Wei Xue and Ivan Yuen and Bernd Möbius},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {12--16},
doi = {10.21437/In... | Receptive multilingualism is a form of communication where speakers can comprehend an utterance of a foreign language (Lx) using their native language (L1) when L1 and Lx share similarities in, e.g., vocabulary and pronunciation. The success of receptive multilingualism can be tested by examining accuracy and reaction ... | null | null |
mohapatra24b_interspeech | 2.5D Vocal Tract Modeling: Bridging Low-Dimensional Efficiency with 3D Accuracy | [
"Debasish Ray Mohapatra",
"Victor Zappi",
"Sidney Fels"
] | https://www.isca-archive.org/interspeech_2024/mohapatra24b_interspeech.html | https://www.isca-archive.org/interspeech_2024/mohapatra24b_interspeech.pdf | 10.21437/Interspeech.2024-1749 | 17-21 | @inproceedings{mohapatra24b_interspeech,
title = {{2.5D Vocal Tract Modeling: Bridging Low-Dimensional Efficiency with 3D Accuracy}},
author = {Debasish Ray Mohapatra and Victor Zappi and Sidney Fels},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {17--21},
doi = {10.21437/I... | We introduce an extended 2D (2.5D) wave solver that blends the computational efficiency of low-dimensional models with the accuracy of 3D approaches tailored for simulating tube geometries similar to vocal tracts. Unlike 1D and 2D models limited to radial symmetry, our lightweight 2.5D finite-difference time-domain sol... | null | null |
chowdhury24_interspeech | Investigating Confidence Estimation Measures for Speaker Diarization | [
"Anurag Chowdhury",
"Abhinav Misra",
"Mark C. Fuhs",
"Monika Woszczyna"
] | https://www.isca-archive.org/interspeech_2024/chowdhury24_interspeech.html | https://www.isca-archive.org/interspeech_2024/chowdhury24_interspeech.pdf | 10.21437/Interspeech.2024-1044 | 22-26 | @inproceedings{chowdhury24_interspeech,
title = {{Investigating Confidence Estimation Measures for Speaker Diarization}},
author = {Anurag Chowdhury and Abhinav Misra and Mark C. Fuhs and Monika Woszczyna},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {22--26},
doi = {10.21... | Speaker diarization systems segment a conversation recording based on the speakers' identity. Such systems can misclassify the speaker of a portion of audio due to a variety of factors, such as speech pattern variation, background noise, and overlapping speech. These errors propagate to, and can adversely affect, downs... | 2406.17124 | title_snapshot |
li24x_interspeech | Speakers Unembedded: Embedding-free Approach to Long-form Neural Diarization | [
"Xiang Li",
"Vivek Govindan",
"Rohit Paturi",
"Sundararajan Srinivasan"
] | https://www.isca-archive.org/interspeech_2024/li24x_interspeech.html | https://www.isca-archive.org/interspeech_2024/li24x_interspeech.pdf | 10.21437/Interspeech.2024-1174 | 27-31 | @inproceedings{li24x_interspeech,
title = {{Speakers Unembedded: Embedding-free Approach to Long-form Neural Diarization}},
author = {Xiang Li and Vivek Govindan and Rohit Paturi and Sundararajan Srinivasan},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {27--31},
doi = {10.... | End-to-end neural diarization (EEND) models offer significant improvements over traditional embedding-based Speaker Diarization (SD) approaches but falls short on generalizing to long-form audio with large number of speakers. EEND-vector-clustering method mitigates this by combining local EEND with global clustering of... | 2406.18679 | title_snapshot |
huang24d_interspeech | On the Success and Limitations of Auxiliary Network Based Word-Level End-to-End Neural Speaker Diarization | [
"Yiling Huang",
"Weiran Wang",
"Guanlong Zhao",
"Hank Liao",
"Wei Xia",
"Quan Wang"
] | https://www.isca-archive.org/interspeech_2024/huang24d_interspeech.html | https://www.isca-archive.org/interspeech_2024/huang24d_interspeech.pdf | 10.21437/Interspeech.2024-561 | 32-36 | @inproceedings{huang24d_interspeech,
title = {{On the Success and Limitations of Auxiliary Network Based Word-Level End-to-End Neural Speaker Diarization}},
author = {Yiling Huang and Weiran Wang and Guanlong Zhao and Hank Liao and Wei Xia and Quan Wang},
year = {2024},
booktitle = {{Interspeech 202... | While standard speaker diarization attempts to answer the question "who spoke when", many realistic applications are interested in determining "who spoke what". In both the conventional modularized approach and the more recent end-to-end neural diarization (EEND), an additional automatic speech recognition (ASR) model ... | 2309.08489 | title_judge |
harkonen24_interspeech | EEND-M2F: Masked-attention mask transformers for speaker diarization | [
"Marc Härkönen",
"Samuel J. Broughton",
"Lahiru Samarakoon"
] | https://www.isca-archive.org/interspeech_2024/harkonen24_interspeech.html | https://www.isca-archive.org/interspeech_2024/harkonen24_interspeech.pdf | 10.21437/Interspeech.2024-668 | 37-41 | @inproceedings{harkonen24_interspeech,
title = {{EEND-M2F: Masked-attention mask transformers for speaker diarization}},
author = {Marc Härkönen and Samuel J. Broughton and Lahiru Samarakoon},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {37--41},
doi = {10.21437/Interspe... | In this paper, we make the explicit connection between image segmentation methods and end-to-end diarization methods. From these insights, we propose a novel, fully end-to-end diarization model, EEND-M2F, based on the Mask2Former architecture. Speaker representations are computed in parallel using a stack of transforme... | 2401.12600 | title_snapshot |
yin24_interspeech | AFL-Net: Integrating Audio, Facial, and Lip Modalities with a Two-step Cross-attention for Robust Speaker Diarization in the Wild | [
"YongKang Yin",
"Xu Li",
"Ying Shan",
"YueXian Zou"
] | https://www.isca-archive.org/interspeech_2024/yin24_interspeech.html | https://www.isca-archive.org/interspeech_2024/yin24_interspeech.pdf | 10.21437/Interspeech.2024-764 | 42-46 | @inproceedings{yin24_interspeech,
title = {{AFL-Net: Integrating Audio, Facial, and Lip Modalities with a Two-step Cross-attention for Robust Speaker Diarization in the Wild}},
author = {YongKang Yin and Xu Li and Ying Shan and YueXian Zou},
year = {2024},
booktitle = {{Interspeech 2024}},
pages ... | Speaker diarization in real-world videos presents significant challenges due to varying acoustic conditions, diverse scenes, the presence of off-screen speakers, etc. This paper builds upon a previous study (AVR-Net) and introduces a novel multi-modal speaker diarization system, AFL-Net. The proposed AFL-Net incorporat... | 2312.05730 | title_snapshot |
arya24_interspeech | Exploiting Wavelet Scattering Transform for an Unsupervised Speaker Diarization in Deep Neural Network Framework | [
"Arunav Arya",
"Murtiza Ali",
"Karan Nathwani"
] | https://www.isca-archive.org/interspeech_2024/arya24_interspeech.html | https://www.isca-archive.org/interspeech_2024/arya24_interspeech.pdf | 10.21437/Interspeech.2024-1146 | 47-51 | @inproceedings{arya24_interspeech,
title = {{Exploiting Wavelet Scattering Transform for an Unsupervised Speaker Diarization in Deep Neural Network Framework}},
author = {Arunav Arya and Murtiza Ali and Karan Nathwani},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {47--51},
doi ... | Advancements in diarization have prompted the development of supervised learning models. These models extract fixed-length embeddings from audio files of varying lengths. Despite challenges, commercial API models like Speechbrain, Resemblyzer, Whisper AI, and Pyannote have addressed this issue. However, these models ty... | null | null |
zhao24h_interspeech | MINT: Boosting Audio-Language Model via Multi-Target Pre-Training and Instruction Tuning | [
"Hang Zhao",
"Yifei Xin",
"Zhesong Yu",
"Bilei Zhu",
"Lu Lu",
"Zejun Ma"
] | https://www.isca-archive.org/interspeech_2024/zhao24h_interspeech.html | https://www.isca-archive.org/interspeech_2024/zhao24h_interspeech.pdf | 10.21437/Interspeech.2024-1863 | 52-56 | @inproceedings{zhao24h_interspeech,
title = {{MINT: Boosting Audio-Language Model via Multi-Target Pre-Training and Instruction Tuning}},
author = {Hang Zhao and Yifei Xin and Zhesong Yu and Bilei Zhu and Lu Lu and Zejun Ma},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {52--56},
... | In the realm of audio-language pre-training (ALP), the challenge of achieving cross-modal alignment is significant. Moreover, the integration of audio inputs with diverse distributions and task variations poses challenges in developing generic audio-language models. In this study, we present MINT, a novel ALP framework... | 2402.07485 | title_snapshot |
niizumi24_interspeech | M2D-CLAP: Masked Modeling Duo Meets CLAP for Learning General-purpose Audio-Language Representation | [
"Daisuke Niizumi",
"Daiki Takeuchi",
"Yasunori Ohishi",
"Noboru Harada",
"Masahiro Yasuda",
"Shunsuke Tsubaki",
"Keisuke Imoto"
] | https://www.isca-archive.org/interspeech_2024/niizumi24_interspeech.html | https://www.isca-archive.org/interspeech_2024/niizumi24_interspeech.pdf | 10.21437/Interspeech.2024-29 | 57-61 | @inproceedings{niizumi24_interspeech,
title = {{M2D-CLAP: Masked Modeling Duo Meets CLAP for Learning General-purpose Audio-Language Representation}},
author = {Daisuke Niizumi and Daiki Takeuchi and Yasunori Ohishi and Noboru Harada and Masahiro Yasuda and Shunsuke Tsubaki and Keisuke Imoto},
year = ... | Contrastive language-audio pre-training (CLAP) enables zero-shot (ZS) inference of audio and exhibits promising performance in several classification tasks. However, conventional audio representations are still crucial for many tasks where ZS is not applicable (e.g., regression problems). Here, we explore a new represe... | 2406.02032 | title_snapshot |
fujita24_interspeech | Audio Fingerprinting with Holographic Reduced Representations | [
"Yusuke Fujita",
"Tatsuya Komatsu"
] | https://www.isca-archive.org/interspeech_2024/fujita24_interspeech.html | https://www.isca-archive.org/interspeech_2024/fujita24_interspeech.pdf | 10.21437/Interspeech.2024-245 | 62-66 | @inproceedings{fujita24_interspeech,
title = {{Audio Fingerprinting with Holographic Reduced Representations}},
author = {Yusuke Fujita and Tatsuya Komatsu},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {62--66},
doi = {10.21437/Interspeech.2024-245},
issn = {2958-17... | This paper proposes an audio fingerprinting model with holographic reduced representation (HRR). The proposed method reduces the number of stored fingerprints, whereas conventional neural audio fingerprinting requires many fingerprints for each audio track to achieve high accuracy and time resolution. We utilize HRR to... | 2406.13139 | title_snapshot |
meyer24b_interspeech | RAST: A Reference-Audio Synchronization Tool for Dubbed Content | [
"David Meyer",
"Eitan Abecassis",
"Clara Fernandez-Labrador",
"Christopher Schroers"
] | https://www.isca-archive.org/interspeech_2024/meyer24b_interspeech.html | https://www.isca-archive.org/interspeech_2024/meyer24b_interspeech.pdf | 10.21437/Interspeech.2024-2203 | 67-71 | @inproceedings{meyer24b_interspeech,
title = {{RAST: A Reference-Audio Synchronization Tool for Dubbed Content}},
author = {David Meyer and Eitan Abecassis and Clara Fernandez-Labrador and Christopher Schroers},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {67--71},
doi = {... | In the film industry, audio-video synchronization issues are considered major quality defects and key drivers of viewer disengagement. This is especially true for dubbed content, which is more prone to these errors due to the added manual process of replacing the original speech with a translated version. Despite their... | null | null |
li24ja_interspeech | YOLOPitch: A Time-Frequency Dual-Branch YOLO Model for Pitch Estimation | [
"Xuefei Li",
"Hao Huang",
"Ying Hu",
"Liang He",
"Jiabao Zhang",
"Yuyi Wang"
] | https://www.isca-archive.org/interspeech_2024/li24ja_interspeech.html | https://www.isca-archive.org/interspeech_2024/li24ja_interspeech.pdf | 10.21437/Interspeech.2024-1805 | 72-76 | @inproceedings{li24ja_interspeech,
title = {{YOLOPitch: A Time-Frequency Dual-Branch YOLO Model for Pitch Estimation}},
author = {Xuefei Li and Hao Huang and Ying Hu and Liang He and Jiabao Zhang and Yuyi Wang},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {72--76},
doi = {... | Pitch estimation is of fundamental importance in audio processing and music information retrieval. YOLO is a well developed model designed for image target detection. Here we introduce YOLOv7 into pitch estimation task and improve by proposing time-frequency (TF) dual-branch into the model according to pitch perception... | null | null |
ullah24_interspeech | Reduce, Reuse, Recycle: Is Perturbed Data Better than Other Language Augmentation for Low Resource Self-Supervised Speech Models | [
"Asad Ullah",
"Alessandro Ragano",
"Andrew Hines"
] | https://www.isca-archive.org/interspeech_2024/ullah24_interspeech.html | https://www.isca-archive.org/interspeech_2024/ullah24_interspeech.pdf | 10.21437/Interspeech.2024-396 | 77-81 | @inproceedings{ullah24_interspeech,
title = {{Reduce, Reuse, Recycle: Is Perturbed Data Better than Other Language Augmentation for Low Resource Self-Supervised Speech Models}},
author = {Asad Ullah and Alessandro Ragano and Andrew Hines},
year = {2024},
booktitle = {{Interspeech 2024}},
pages ... | Self-supervised representation learning (SSRL) has demonstrated superior performance than supervised models for tasks including phoneme recognition. Training SSRL models poses a challenge for low-resource languages where sufficient pre-training data may not be available. A common approach is cross-lingual pre-training.... | 2309.12763 | title_snapshot |
pieper24_interspeech | AlignNet: Learning dataset score alignment functions to enable better training of speech quality estimators | [
"Jaden Pieper",
"Stephen Voran"
] | https://www.isca-archive.org/interspeech_2024/pieper24_interspeech.html | https://www.isca-archive.org/interspeech_2024/pieper24_interspeech.pdf | 10.21437/Interspeech.2024-74 | 82-86 | @inproceedings{pieper24_interspeech,
title = {{AlignNet: Learning dataset score alignment functions to enable better training of speech quality estimators}},
author = {Jaden Pieper and Stephen Voran},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {82--86},
doi = {10.21437/In... | We develop two complementary advances for training no-reference (NR) speech quality estimators with independent datasets. Multi-dataset finetuning (MDF) pretrains an NR estimator on a single dataset and then finetunes it on multiple datasets at once, including the dataset used for pretraining. AlignNet uses an AudioNet... | 2406.10205 | title_snapshot |
liang24_interspeech | Improving Audio Classification with Low-Sampled Microphone Input: An Empirical Study Using Model Self-Distillation | [
"Dawei Liang",
"Alice Zhang",
"David Harwath",
"Edison Thomaz"
] | https://www.isca-archive.org/interspeech_2024/liang24_interspeech.html | https://www.isca-archive.org/interspeech_2024/liang24_interspeech.pdf | 10.21437/Interspeech.2024-2285 | 87-91 | @inproceedings{liang24_interspeech,
title = {{Improving Audio Classification with Low-Sampled Microphone Input: An Empirical Study Using Model Self-Distillation}},
author = {Dawei Liang and Alice Zhang and David Harwath and Edison Thomaz},
year = {2024},
booktitle = {{Interspeech 2024}},
pages ... | Acoustic scene and event classification is gaining traction in mobile health and wearable applications. Traditionally, relevant research focused on high-quality inputs (sampling rates >= 16 kHz). However, lower sampling rates (e.g., 1 kHz - 2 kHz) offer enhanced privacy and reduced power consumption, crucial for contin... | null | null |
mu24_interspeech | MFF-EINV2: Multi-scale Feature Fusion across Spectral-Spatial-Temporal Domains for Sound Event Localization and Detection | [
"Da Mu",
"Zhicheng Zhang",
"Haobo Yue"
] | https://www.isca-archive.org/interspeech_2024/mu24_interspeech.html | https://www.isca-archive.org/interspeech_2024/mu24_interspeech.pdf | 10.21437/Interspeech.2024-145 | 92-96 | @inproceedings{mu24_interspeech,
title = {{MFF-EINV2: Multi-scale Feature Fusion across Spectral-Spatial-Temporal Domains for Sound Event Localization and Detection}},
author = {Da Mu and Zhicheng Zhang and Haobo Yue},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {92--96},
doi ... | Sound Event Localization and Detection (SELD) involves detecting and localizing sound events using multichannel sound recordings. Previously proposed Event-Independent Network V2 (EINV2) has achieved outstanding performance on SELD. However, it still faces challenges in effectively extracting features across spectral, ... | 2406.08771 | title_snapshot |
nam24_interspeech | Diversifying and Expanding Frequency-Adaptive Convolution Kernels for Sound Event Detection | [
"Hyeonuk Nam",
"Seong-Hu Kim",
"Deokki Min",
"Junhyeok Lee",
"Yong-Hwa Park"
] | https://www.isca-archive.org/interspeech_2024/nam24_interspeech.html | https://www.isca-archive.org/interspeech_2024/nam24_interspeech.pdf | 10.21437/Interspeech.2024-216 | 97-101 | @inproceedings{nam24_interspeech,
title = {{Diversifying and Expanding Frequency-Adaptive Convolution Kernels for Sound Event Detection}},
author = {Hyeonuk Nam and Seong-Hu Kim and Deokki Min and Junhyeok Lee and Yong-Hwa Park},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {97--10... | Frequency dynamic convolution (FDY conv) has shown the state-of-the-art performance in sound event detection (SED) using frequency-adaptive kernels obtained by frequency-varying combination of basis kernels. However, FDY conv lacks an explicit mean to diversify frequency-adaptive kernels, potentially limiting the perfo... | 2406.05341 | title_snapshot |
ho24_interspeech | Stream-based Active Learning for Anomalous Sound Detection in Machine Condition Monitoring | [
"Tuan Vu Ho",
"Kota Dohi",
"Yohei Kawaguchi"
] | https://www.isca-archive.org/interspeech_2024/ho24_interspeech.html | https://www.isca-archive.org/interspeech_2024/ho24_interspeech.pdf | 10.21437/Interspeech.2024-573 | 102-106 | @inproceedings{ho24_interspeech,
title = {{Stream-based Active Learning for Anomalous Sound Detection in Machine Condition Monitoring}},
author = {Tuan Vu Ho and Kota Dohi and Yohei Kawaguchi},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {102--106},
doi = {10.21437/Intersp... | This paper introduces an active learning (AL) framework for anomalous sound detection (ASD) in machine condition monitoring system. Typically, ASD models are trained solely on normal samples due to the scarcity of anomalous data, leading to decreased accuracy for unseen samples during inference. AL is a promising solut... | 2408.05493 | title_snapshot |
jiang24c_interspeech | AnoPatch: Towards Better Consistency in Machine Anomalous Sound Detection | [
"Anbai Jiang",
"Bing Han",
"Zhiqiang Lv",
"Yufeng Deng",
"Wei-Qiang Zhang",
"Xie Chen",
"Yanmin Qian",
"Jia Liu",
"Pingyi Fan"
] | https://www.isca-archive.org/interspeech_2024/jiang24c_interspeech.html | https://www.isca-archive.org/interspeech_2024/jiang24c_interspeech.pdf | 10.21437/Interspeech.2024-1761 | 107-111 | @inproceedings{jiang24c_interspeech,
title = {{AnoPatch: Towards Better Consistency in Machine Anomalous Sound Detection}},
author = {Anbai Jiang and Bing Han and Zhiqiang Lv and Yufeng Deng and Wei-Qiang Zhang and Xie Chen and Yanmin Qian and Jia Liu and Pingyi Fan},
year = {2024},
booktitle = {{In... | Large pre-trained models have demonstrated dominant performances in multiple areas, where the consistency between pre-training and fine-tuning is the key to success. However, few works reported satisfactory results of pre-trained models for the machine anomalous sound detection (ASD) task. This may be caused by the inc... | 2406.11364 | title_snapshot |
xie24d_interspeech | FakeSound: Deepfake General Audio Detection | [
"Zeyu Xie",
"Baihan Li",
"Xuenan Xu",
"Zheng Liang",
"Kai Yu",
"Mengyue Wu"
] | https://www.isca-archive.org/interspeech_2024/xie24d_interspeech.html | https://www.isca-archive.org/interspeech_2024/xie24d_interspeech.pdf | 10.21437/Interspeech.2024-1703 | 112-116 | @inproceedings{xie24d_interspeech,
title = {{FakeSound: Deepfake General Audio Detection}},
author = {Zeyu Xie and Baihan Li and Xuenan Xu and Zheng Liang and Kai Yu and Mengyue Wu},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {112--116},
doi = {10.21437/Interspeech.2024-1... | With the advancement of audio generation, generative models can produce highly realistic audios. However, the proliferation of deepfake general audio can pose negative consequences. Therefore, we propose a new task, deepfake general audio detection, which aims to identify whether audio content is manipulated and to loc... | 2406.08052 | title_snapshot |
ghaffarzadegan24_interspeech | Sound of Traffic: A Dataset for Acoustic Traffic Identification and Counting | [
"Shabnam Ghaffarzadegan",
"Luca Bondi",
"Wei-Chang Lin",
"Abinaya Kumar",
"Ho-Hsiang Wu",
"Hans-Georg Horst",
"Samarjit Das"
] | https://www.isca-archive.org/interspeech_2024/ghaffarzadegan24_interspeech.html | https://www.isca-archive.org/interspeech_2024/ghaffarzadegan24_interspeech.pdf | 10.21437/Interspeech.2024-1205 | 117-121 | @inproceedings{ghaffarzadegan24_interspeech,
title = {{Sound of Traffic: A Dataset for Acoustic Traffic Identification and Counting}},
author = {Shabnam Ghaffarzadegan and Luca Bondi and Wei-Chang Lin and Abinaya Kumar and Ho-Hsiang Wu and Hans-Georg Horst and Samarjit Das},
year = {2024},
booktitle... | We introduce Sound of Traffic, the largest publicly available dataset for traffic identification and counting to date. With over 415 hours of multichannel acoustic traffic data recorded in six different locations, it encompasses varying levels of traffic density and environmental conditions. In this work, we discuss st... | null | null |
kumar24_interspeech | Vision Transformer Segmentation for Visual Bird Sound Denoising | [
"Sahil Kumar",
"Jialu Li",
"Youshan Zhang"
] | https://www.isca-archive.org/interspeech_2024/kumar24_interspeech.html | https://www.isca-archive.org/interspeech_2024/kumar24_interspeech.pdf | 10.21437/Interspeech.2024-1412 | 122-126 | @inproceedings{kumar24_interspeech,
title = {{Vision Transformer Segmentation for Visual Bird Sound Denoising}},
author = {Sahil Kumar and Jialu Li and Youshan Zhang},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {122--126},
doi = {10.21437/Interspeech.2024-1412},
issn ... | Audio denoising, especially in the context of bird sounds, remains a challenging task due to persistent residual noise. Traditional and deep learning methods often struggle with artificial or low-frequency noise. In this work, we propose ViTVS, a novel approach that leverages the power of the vision transformer (ViT) a... | 2406.09167 | title_snapshot |
jing24_interspeech | DB3V: A Dialect Dominated Dataset of Bird Vocalisation for Cross-corpus Bird Species Recognition | [
"Xin Jing",
"Luyang Zhang",
"Jiangjian Xie",
"Alexander Gebhard",
"Alice Baird",
"Björn Schuller"
] | https://www.isca-archive.org/interspeech_2024/jing24_interspeech.html | https://www.isca-archive.org/interspeech_2024/jing24_interspeech.pdf | 10.21437/Interspeech.2024-143 | 127-131 | @inproceedings{jing24_interspeech,
title = {{DB3V: A Dialect Dominated Dataset of Bird Vocalisation for Cross-corpus Bird Species Recognition}},
author = {Xin Jing and Luyang Zhang and Jiangjian Xie and Alexander Gebhard and Alice Baird and Björn Schuller},
year = {2024},
booktitle = {{Interspeech ... | In ornithology, bird species are known to have varieditâs widely acknowledged that bird species display diverse dialects in their calls across different regions. Consequently, computational methods to identify bird species onsolely through their calls face critsignificalnt challenges. There is growing interest in und... | 2406.08517 | title_snapshot |
cauzinille24_interspeech | Investigating self-supervised speech models' ability to classify animal vocalizations: The case of gibbon's vocal signatures | [
"Jules Cauzinille",
"Benoît Favre",
"Ricard Marxer",
"Dena Clink",
"Abdul Hamid Ahmad",
"Arnaud Rey"
] | https://www.isca-archive.org/interspeech_2024/cauzinille24_interspeech.html | https://www.isca-archive.org/interspeech_2024/cauzinille24_interspeech.pdf | 10.21437/Interspeech.2024-1096 | 132-136 | @inproceedings{cauzinille24_interspeech,
title = {{Investigating self-supervised speech models' ability to classify animal vocalizations: The case of gibbon's vocal signatures}},
author = {Jules Cauzinille and Benoît Favre and Ricard Marxer and Dena Clink and Abdul Hamid Ahmad and Arnaud Rey},
year =... | With the advent of pre-trained self-supervised learning (SSL) models, speech processing research is showing increasing interest towards disentanglement and explainability. Amongst other methods, probing speech classifiers has emerged as a promising approach to gain new insights into SSL models out-of-domain performance... | null | null |
qiu24_interspeech | Study Selectively: An Adaptive Knowledge Distillation based on a Voting Network for Heart Sound Classification | [
"Xihang Qiu",
"Lixian Zhu",
"Zikai Song",
"Zeyu Chen",
"Haojie Zhang",
"Kun Qian",
"Ye Zhang",
"Bin Hu",
"Yoshiharu Yamamoto",
"Björn W. Schuller"
] | https://www.isca-archive.org/interspeech_2024/qiu24_interspeech.html | https://www.isca-archive.org/interspeech_2024/qiu24_interspeech.pdf | 10.21437/Interspeech.2024-439 | 137-141 | @inproceedings{qiu24_interspeech,
title = {{Study Selectively: An Adaptive Knowledge Distillation based on a Voting Network for Heart Sound Classification}},
author = {Xihang Qiu and Lixian Zhu and Zikai Song and Zeyu Chen and Haojie Zhang and Kun Qian and Ye Zhang and Bin Hu and Yoshiharu Yamamoto and Björ... | Phonocardiogram classification methods using deep neural networks have been widely applied to the early detection of cardiovascular diseases recently. Despite their excellent recognition rate, the sizeable computational complexity limits their further development. Nowadays, knowledge distillation (KD) is an established... | null | null |
lin24_interspeech | SimuSOE: A Simulated Snoring Dataset for Obstructive Sleep Apnea-Hypopnea Syndrome Evaluation during Wakefulness | [
"Jie Lin",
"Xiuping Yang",
"Li Xiao",
"Xinhong Li",
"Weiyan Yi",
"Yuhong Yang",
"Weiping Tu",
"Xiong Chen"
] | https://www.isca-archive.org/interspeech_2024/lin24_interspeech.html | https://www.isca-archive.org/interspeech_2024/lin24_interspeech.pdf | 10.21437/Interspeech.2024-283 | 142-146 | @inproceedings{lin24_interspeech,
title = {{SimuSOE: A Simulated Snoring Dataset for Obstructive Sleep Apnea-Hypopnea Syndrome Evaluation during Wakefulness}},
author = {Jie Lin and Xiuping Yang and Li Xiao and Xinhong Li and Weiyan Yi and Yuhong Yang and Weiping Tu and Xiong Chen},
year = {2024},
b... | Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a prevalent chronic breathing disorder caused by upper airway obstruction. Previous studies advanced OSAHS evaluation through machine learning-based systems trained on sleep snoring or speech signal datasets. However, constructing datasets for training a precise and ... | 2407.07397 | title_snapshot |
nayak24_interspeech | Multi-mic Echo Cancellation Coalesced with Beamforming for Real World Adverse Acoustic Conditions | [
"Premanand Nayak",
"Kamini Sabu",
"M. Ali Basha Shaik"
] | https://www.isca-archive.org/interspeech_2024/nayak24_interspeech.html | https://www.isca-archive.org/interspeech_2024/nayak24_interspeech.pdf | 10.21437/Interspeech.2024-834 | 147-151 | @inproceedings{nayak24_interspeech,
title = {{Multi-mic Echo Cancellation Coalesced with Beamforming for Real World Adverse Acoustic Conditions}},
author = {Premanand Nayak and Kamini Sabu and M. Ali Basha Shaik},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {147--151},
doi ... | Robust acoustic echo cancellation (AEC) is essential for voice enabled smart devices. Multi-channel signals are used in AEC along with beamformer (BF) for better residual echo suppression (RES). In this work, we introduce a deep neural network (DNN) based novel unified framework for multi-microphone AEC (MMAEC) and RES... | null | null |
khanagha24_interspeech | Interference Aware Training Target for DNN based joint Acoustic Echo Cancellation and Noise Suppression | [
"Vahid Khanagha",
"Dimitris Koutsaidis",
"Kaustubh Kalgaonkar",
"Sriram Srinivasan"
] | https://www.isca-archive.org/interspeech_2024/khanagha24_interspeech.html | https://www.isca-archive.org/interspeech_2024/khanagha24_interspeech.pdf | 10.21437/Interspeech.2024-1414 | 152-156 | @inproceedings{khanagha24_interspeech,
title = {{Interference Aware Training Target for DNN based joint Acoustic Echo Cancellation and Noise Suppression}},
author = {Vahid Khanagha and Dimitris Koutsaidis and Kaustubh Kalgaonkar and Sriram Srinivasan},
year = {2024},
booktitle = {{Interspeech 2024}}... | Despite remarkable performance of Deep Learning based Acoustic Echo Cancellation (AEC) systems, effective handling of double-talk scenarios remains a challenge. During double-talk the speech signal from the far-end talker overlaps with the target near-end speech and results in degraded performance in form of near-end s... | null | null |
gao24b_interspeech | Low Complexity Echo Delay Estimator Based on Binarized Feature Matching | [
"Yi Gao",
"Xiang Su"
] | https://www.isca-archive.org/interspeech_2024/gao24b_interspeech.html | https://www.isca-archive.org/interspeech_2024/gao24b_interspeech.pdf | 10.21437/Interspeech.2024-107 | 157-161 | @inproceedings{gao24b_interspeech,
title = {{Low Complexity Echo Delay Estimator Based on Binarized Feature Matching}},
author = {Yi Gao and Xiang Su},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {157--161},
doi = {10.21437/Interspeech.2024-107},
issn = {2958-1796},... | Echo delay estimation (EDE) serves as a preprocessing component within an acoustic echo canceller (AEC). Despite some progress over the past few decades, there is a dearth of literature on efficient algorithms. This paper introduces a binarized feature-matching (BFM) framework, encompassing a set of feature extraction ... | null | null |
ni24_interspeech | MSA-DPCRN: A Multi-Scale Asymmetric Dual-Path Convolution Recurrent Network with Attentional Feature Fusion for Acoustic Echo Cancellation | [
"Ye Ni",
"Cong Pang",
"Chengwei Huang",
"Cairong Zou"
] | https://www.isca-archive.org/interspeech_2024/ni24_interspeech.html | https://www.isca-archive.org/interspeech_2024/ni24_interspeech.pdf | 10.21437/Interspeech.2024-1355 | 162-166 | @inproceedings{ni24_interspeech,
title = {{MSA-DPCRN: A Multi-Scale Asymmetric Dual-Path Convolution Recurrent Network with Attentional Feature Fusion for Acoustic Echo Cancellation}},
author = {Ye Ni and Cong Pang and Chengwei Huang and Cairong Zou},
year = {2024},
booktitle = {{Interspeech 2024}},... | Echo cancellation plays a crucial role in modern speech applications. Numerous deep-learning models have been developed for the echo cancellation task and achieved great progress by incorporating additional features; however, the majority of these models overlook the characteristics of different features and simply mer... | null | null |
schwartz24_interspeech | Efficient Joint Bemforming and Acoustic Echo Cancellation Structure for Conference Call Scenarios | [
"Ofer Schwartz",
"Sharon Gannot"
] | https://www.isca-archive.org/interspeech_2024/schwartz24_interspeech.html | https://www.isca-archive.org/interspeech_2024/schwartz24_interspeech.pdf | 10.21437/Interspeech.2024-1957 | 167-171 | @inproceedings{schwartz24_interspeech,
title = {{Efficient Joint Bemforming and Acoustic Echo Cancellation Structure for Conference Call Scenarios}},
author = {Ofer Schwartz and Sharon Gannot},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {167--171},
doi = {10.21437/Intersp... | We propose an efficient scheme for combining beamformer (BF) and acoustic echo cancellation (AEC). We focus on conference call scenarios characterized by stationary background noise and multiple speakers who alternate frequently. Furthermore, aiming at low-resource devices, a common strategy is to apply a single AEC at... | null | null |
zhao24b_interspeech | SDAEC: Signal Decoupling for Advancing Acoustic Echo Cancellation | [
"Fei Zhao",
"Jinjiang Liu",
"Xueliang Zhang"
] | https://www.isca-archive.org/interspeech_2024/zhao24b_interspeech.html | https://www.isca-archive.org/interspeech_2024/zhao24b_interspeech.pdf | 10.21437/Interspeech.2024-763 | 172-176 | @inproceedings{zhao24b_interspeech,
title = {{SDAEC: Signal Decoupling for Advancing Acoustic Echo Cancellation}},
author = {Fei Zhao and Jinjiang Liu and Xueliang Zhang},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {172--176},
doi = {10.21437/Interspeech.2024-763},
issn... | In deep learning-based acoustic echo cancellation methods, neural networks implicitly learn echo paths to cancel echoes. However, under low signal-to-echo ratio conditions, the substantial energy discrepancy between the microphone signal and the reference signal impedes the network's ability, resulting in poor performa... | null | null |
seki24_interspeech | Spatial Voice Conversion: Voice Conversion Preserving Spatial Information and Non-target Signals | [
"Kentaro Seki",
"Shinnosuke Takamichi",
"Norihiro Takamune",
"Yuki Saito",
"Kanami Imamura",
"Hiroshi Saruwatari"
] | https://www.isca-archive.org/interspeech_2024/seki24_interspeech.html | https://www.isca-archive.org/interspeech_2024/seki24_interspeech.pdf | 10.21437/Interspeech.2024-1107 | 177-181 | @inproceedings{seki24_interspeech,
title = {{Spatial Voice Conversion: Voice Conversion Preserving Spatial Information and Non-target Signals}},
author = {Kentaro Seki and Shinnosuke Takamichi and Norihiro Takamune and Yuki Saito and Kanami Imamura and Hiroshi Saruwatari},
year = {2024},
booktitle =... | This paper proposes a new task called spatial voice conversion, which aims to convert a target voice while preserving spatial information and non-target signals. Traditional voice conversion methods focus on single-channel waveforms, ignoring the stereo listening experience inherent in human hearing. Our baseline appro... | 2406.17722 | title_snapshot |
baade24_interspeech | Neural Codec Language Models for Disentangled and Textless Voice Conversion | [
"Alan Baade",
"Puyuan Peng",
"David Harwath"
] | https://www.isca-archive.org/interspeech_2024/baade24_interspeech.html | https://www.isca-archive.org/interspeech_2024/baade24_interspeech.pdf | 10.21437/Interspeech.2024-1298 | 182-186 | @inproceedings{baade24_interspeech,
title = {{Neural Codec Language Models for Disentangled and Textless Voice Conversion}},
author = {Alan Baade and Puyuan Peng and David Harwath},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {182--186},
doi = {10.21437/Interspeech.2024-12... | We introduce a method for textless any-to-any voice conversion based on the recent progress in speech synthesis driven by neural codec language models. To disentangle the speaker and linguistic information, we adapt a speaker normalizing procedure for discrete semantic units, and then generate with an autoregressive la... | null | null |
morrison24_interspeech | Fine-Grained and Interpretable Neural Speech Editing | [
"Max Morrison",
"Cameron Churchwell",
"Nathan Pruyne",
"Bryan Pardo"
] | https://www.isca-archive.org/interspeech_2024/morrison24_interspeech.html | https://www.isca-archive.org/interspeech_2024/morrison24_interspeech.pdf | 10.21437/Interspeech.2024-2351 | 187-191 | @inproceedings{morrison24_interspeech,
title = {{Fine-Grained and Interpretable Neural Speech Editing}},
author = {Max Morrison and Cameron Churchwell and Nathan Pruyne and Bryan Pardo},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {187--191},
doi = {10.21437/Interspeech.20... | Fine-grained editing of speech attributes - such as prosody (i.e., the pitch, loudness, and phoneme durations), pronunciation, speaker identity, and formants - is useful for fine-tuning and fixing imperfections in human and AI-generated speech recordings for creation of podcasts, film dialogue, and video game dialogue.... | 2407.05471 | title_snapshot |
kaneko24_interspeech | FastVoiceGrad: One-step Diffusion-Based Voice Conversion with Adversarial Conditional Diffusion Distillation | [
"Takuhiro Kaneko",
"Hirokazu Kameoka",
"Kou Tanaka",
"Yuto Kondo"
] | https://www.isca-archive.org/interspeech_2024/kaneko24_interspeech.html | https://www.isca-archive.org/interspeech_2024/kaneko24_interspeech.pdf | 10.21437/Interspeech.2024-2387 | 192-196 | @inproceedings{kaneko24_interspeech,
title = {{FastVoiceGrad: One-step Diffusion-Based Voice Conversion with Adversarial Conditional Diffusion Distillation}},
author = {Takuhiro Kaneko and Hirokazu Kameoka and Kou Tanaka and Yuto Kondo},
year = {2024},
booktitle = {{Interspeech 2024}},
pages =... | Diffusion-based voice conversion (VC) techniques such as VoiceGrad have attracted interest because of their high VC performance in terms of speech quality and speaker similarity. However, a notable limitation is the slow inference caused by the multi-step reverse diffusion. Therefore, we propose FastVoiceGrad, a novel ... | 2409.02245 | title_snapshot |
ning24_interspeech | DualVC 3: Leveraging Language Model Generated Pseudo Context for End-to-end Low Latency Streaming Voice Conversion | [
"Ziqian Ning",
"Shuai Wang",
"Pengcheng Zhu",
"Zhichao Wang",
"Jixun Yao",
"Lei Xie",
"Mengxiao Bi"
] | https://www.isca-archive.org/interspeech_2024/ning24_interspeech.html | https://www.isca-archive.org/interspeech_2024/ning24_interspeech.pdf | 10.21437/Interspeech.2024-1857 | 197-201 | @inproceedings{ning24_interspeech,
title = {{DualVC 3: Leveraging Language Model Generated Pseudo Context for End-to-end Low Latency Streaming Voice Conversion}},
author = {Ziqian Ning and Shuai Wang and Pengcheng Zhu and Zhichao Wang and Jixun Yao and Lei Xie and Mengxiao Bi},
year = {2024},
bookti... | Streaming voice conversion has gained popularity for its applicability in real-time applications. The recently proposed DualVC 2 has successfully achieved robust and high-quality streaming voice conversion in approximately 180ms. However, DualVC 2 is based on the recognition-synthesis framework, with multi-level casca... | 2406.07846 | title_snapshot |
qi24_interspeech | Towards Realistic Emotional Voice Conversion using Controllable Emotional Intensity | [
"Tianhua Qi",
"Shiyan Wang",
"Cheng Lu",
"Yan Zhao",
"Yuan Zong",
"Wenming Zheng"
] | https://www.isca-archive.org/interspeech_2024/qi24_interspeech.html | https://www.isca-archive.org/interspeech_2024/qi24_interspeech.pdf | 10.21437/Interspeech.2024-1941 | 202-206 | @inproceedings{qi24_interspeech,
title = {{Towards Realistic Emotional Voice Conversion using Controllable Emotional Intensity}},
author = {Tianhua Qi and Shiyan Wang and Cheng Lu and Yan Zhao and Yuan Zong and Wenming Zheng},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {202--206}... | Realistic emotional voice conversion (EVC) aims to enhance emotional diversity of converted audios, making the synthesized voices more authentic and natural. To this end, we propose Emotional Intensity-aware Network (EINet), dynamically adjusting intonation and rhythm by incorporating controllable emotional intensity. ... | 2407.14800 | title_snapshot |
nakagome24_interspeech | InterBiasing: Boost Unseen Word Recognition through Biasing Intermediate Predictions | [
"Yu Nakagome",
"Michael Hentschel"
] | https://www.isca-archive.org/interspeech_2024/nakagome24_interspeech.html | https://www.isca-archive.org/interspeech_2024/nakagome24_interspeech.pdf | 10.21437/Interspeech.2024-619 | 207-211 | @inproceedings{nakagome24_interspeech,
title = {{InterBiasing: Boost Unseen Word Recognition through Biasing Intermediate Predictions}},
author = {Yu Nakagome and Michael Hentschel},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {207--211},
doi = {10.21437/Interspeech.2024-6... | Despite recent advances in end-to-end speech recognition methods, their output is biased to the training dataâs vocabulary, resulting in inaccurate recognition of unknown terms or proper nouns. To improve the recognition accuracy for a given set of such terms, we propose an adaptation parameter-free approach based on... | 2406.14890 | title_snapshot |
meng24_interspeech | SEQ-former: A context-enhanced and efficient automatic speech recognition framework | [
"Qinglin Meng",
"Min Liu",
"Kaixun Huang",
"Kun Wei",
"Lei Xie",
"Zongfeng Quan",
"Weihong Deng",
"Quan Lu",
"Ning Jiang",
"Guoqing Zhao"
] | https://www.isca-archive.org/interspeech_2024/meng24_interspeech.html | https://www.isca-archive.org/interspeech_2024/meng24_interspeech.pdf | 10.21437/Interspeech.2024-243 | 212-216 | @inproceedings{meng24_interspeech,
title = {{SEQ-former: A context-enhanced and efficient automatic speech recognition framework}},
author = {Qinglin Meng and Min Liu and Kaixun Huang and Kun Wei and Lei Xie and Zongfeng Quan and Weihong Deng and Quan Lu and Ning Jiang and Guoqing Zhao},
year = {2024}... | Contextual information is crucial for automatic speech recognition (ASR). Effective utilization of contextual information can improve the accuracy of ASR systems. To improve the model's ability to capture this information, we propose a novel ASR framework called SEQ-former, emphasizing simplicity, efficiency, and quick... | null | null |
flynn24b_interspeech | How Much Context Does My Attention-Based ASR System Need? | [
"Robert Flynn",
"Anton Ragni"
] | https://www.isca-archive.org/interspeech_2024/flynn24b_interspeech.html | https://www.isca-archive.org/interspeech_2024/flynn24b_interspeech.pdf | 10.21437/Interspeech.2024-870 | 217-221 | @inproceedings{flynn24b_interspeech,
title = {{How Much Context Does My Attention-Based ASR System Need?}},
author = {Robert Flynn and Anton Ragni},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {217--221},
doi = {10.21437/Interspeech.2024-870},
issn = {2958-1796},
} | For the task of speech recognition, the use of more than 30 seconds of acoustic context during training is uncommon and under-investigated in literature. In this work, we conduct an empirical study on the effect of scaling the sequence length used to train/evaluate (dense-attention-based) acoustic models on speech reco... | 2310.15672 | title_snapshot |
vitale24_interspeech | Rich speech signal: exploring and exploiting end-to-end automatic speech recognizersâ ability to model hesitation phenomena | [
"Vincenzo Norman Vitale",
"Loredana Schettino",
"Francesco Cutugno"
] | https://www.isca-archive.org/interspeech_2024/vitale24_interspeech.html | https://www.isca-archive.org/interspeech_2024/vitale24_interspeech.pdf | 10.21437/Interspeech.2024-2029 | 222-226 | @inproceedings{vitale24_interspeech,
title = {{Rich speech signal: exploring and exploiting end-to-end automatic speech recognizersâ ability to model hesitation phenomena}},
author = {Vincenzo Norman Vitale and Loredana Schettino and Francesco Cutugno},
year = {2024},
booktitle = {{Interspeech 20... | Modern automatic speech recognition systems can achieve remarkable performances. However, they usually neglect speech characteristic phenomena such as fillers ( ) or segmental prolongations (the ) which are still only considered as disrupting objects to be detected and removed, despite their acknowledged regularity and... | null | null |
zhang24q_interspeech | Transmitted and Aggregated Self-Attention for Automatic Speech Recognition | [
"Tian-Hao Zhang",
"Xinyuan Qian",
"Feng Chen",
"Xu-Cheng Yin"
] | https://www.isca-archive.org/interspeech_2024/zhang24q_interspeech.html | https://www.isca-archive.org/interspeech_2024/zhang24q_interspeech.pdf | 10.21437/Interspeech.2024-2374 | 227-231 | @inproceedings{zhang24q_interspeech,
title = {{Transmitted and Aggregated Self-Attention for Automatic Speech Recognition}},
author = {Tian-Hao Zhang and Xinyuan Qian and Feng Chen and Xu-Cheng Yin},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {227--231},
doi = {10.21437/I... | Transformer based models have recently achieved outstanding progress in ASR system. The attention maps are generated in self-attention to capture temporal relationships among input tokens and heavily influence transformer performance. Many works demonstrate that attention maps of different layers incorporate various co... | null | null |
prabhu24_interspeech | MULTI-CONVFORMER: Extending Conformer with Multiple Convolution Kernels | [
"Darshan Prabhu",
"Yifan Peng",
"Preethi Jyothi",
"Shinji Watanabe"
] | https://www.isca-archive.org/interspeech_2024/prabhu24_interspeech.html | https://www.isca-archive.org/interspeech_2024/prabhu24_interspeech.pdf | 10.21437/Interspeech.2024-2384 | 232-236 | @inproceedings{prabhu24_interspeech,
title = {{MULTI-CONVFORMER: Extending Conformer with Multiple Convolution Kernels}},
author = {Darshan Prabhu and Yifan Peng and Preethi Jyothi and Shinji Watanabe},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {232--236},
doi = {10.2143... | Convolutions have become essential in state-of-the-art end-to-end Automatic Speech Recognition (ASR) systems due to their efficient modelling of local context. Notably, its use in Conformers has led to superior performance compared to vanilla Transformer-based ASR systems. While components other than the convolution mo... | 2407.03718 | title_snapshot |
miyazaki24_interspeech | Exploring the Capability of Mamba in Speech Applications | [
"Koichi Miyazaki",
"Yoshiki Masuyama",
"Masato Murata"
] | https://www.isca-archive.org/interspeech_2024/miyazaki24_interspeech.html | https://www.isca-archive.org/interspeech_2024/miyazaki24_interspeech.pdf | 10.21437/Interspeech.2024-994 | 237-241 | @inproceedings{miyazaki24_interspeech,
title = {{Exploring the Capability of Mamba in Speech Applications}},
author = {Koichi Miyazaki and Yoshiki Masuyama and Masato Murata},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {237--241},
doi = {10.21437/Interspeech.2024-994},
... | This paper explores the capability of Mamba, a recently proposed architecture based on state space models (SSMs), as a competitive alternative to Transformer-based models. In the speech domain, well-designed Transformer-based models, such as the Conformer and E-Branchformer, have become the de facto standards. Extensiv... | 2406.16808 | title_snapshot |
wan24_interspeech | Lightweight Transducer Based on Frame-Level Criterion | [
"Genshun Wan",
"Mengzhi Wang",
"Tingzhi Mao",
"Hang Chen",
"Zhongfu Ye"
] | https://www.isca-archive.org/interspeech_2024/wan24_interspeech.html | https://www.isca-archive.org/interspeech_2024/wan24_interspeech.pdf | 10.21437/Interspeech.2024-768 | 247-251 | @inproceedings{wan24_interspeech,
title = {{Lightweight Transducer Based on Frame-Level Criterion}},
author = {Genshun Wan and Mengzhi Wang and Tingzhi Mao and Hang Chen and Zhongfu Ye},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {247--251},
doi = {10.21437/Interspeech.20... | The transducer model trained based on sequence-level criterion requires a lot of memory due to the generation of the large probability matrix. We proposed a lightweight transducer model based on frame-level criterion, which uses the results of the CTC forced alignment algorithm to determine the label for each frame. Th... | 2409.13698 | title_snapshot |
gupta24_interspeech | Exploring the limits of decoder-only models trained on public speech recognition corpora | [
"Ankit Gupta",
"George Saon",
"Brian Kingsbury"
] | https://www.isca-archive.org/interspeech_2024/gupta24_interspeech.html | https://www.isca-archive.org/interspeech_2024/gupta24_interspeech.pdf | 10.21437/Interspeech.2024-565 | 252-256 | @inproceedings{gupta24_interspeech,
title = {{Exploring the limits of decoder-only models trained on public speech recognition corpora}},
author = {Ankit Gupta and George Saon and Brian Kingsbury},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {252--256},
doi = {10.21437/Int... | The emergence of industrial-scale automatic speech recognition (ASR) models such as Whisper and USM, trained on 1M hours of weakly labelled and 12M hours of audio only proprietary data respectively, has led to a stronger need for large scale public ASR corpora and competitive open source pipelines. Unlike the said mode... | 2402.00235 | title_snapshot |
gong24b_interspeech | Contextual Biasing Speech Recognition in Speech-enhanced Large Language Model | [
"Xun Gong",
"Anqi Lv",
"Zhiming Wang",
"Yanmin Qian"
] | https://www.isca-archive.org/interspeech_2024/gong24b_interspeech.html | https://www.isca-archive.org/interspeech_2024/gong24b_interspeech.pdf | 10.21437/Interspeech.2024-965 | 257-261 | @inproceedings{gong24b_interspeech,
title = {{Contextual Biasing Speech Recognition in Speech-enhanced Large Language Model}},
author = {Xun Gong and Anqi Lv and Zhiming Wang and Yanmin Qian},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {257--261},
doi = {10.21437/Interspe... | Recently, the rapid advancements in audio- and speech-enhanced large language models (SpeechLLMs), such as Qwen-Audio and SALMONN, have significantly propelled automatic speech recognition (ASR) forward. However, despite the improvements in universal recognition capabilities, bias word recognition persists as a promine... | null | null |
wang24k_interspeech | Towards Effective and Efficient Non-autoregressive Decoding Using Block-based Attention Mask | [
"Tianzi Wang",
"Xurong Xie",
"Zhaoqing Li",
"Shoukang Hu",
"Zengrui Jin",
"Jiajun Deng",
"Mingyu Cui",
"Shujie Hu",
"Mengzhe Geng",
"Guinan Li",
"Helen Meng",
"Xunying Liu"
] | https://www.isca-archive.org/interspeech_2024/wang24k_interspeech.html | https://www.isca-archive.org/interspeech_2024/wang24k_interspeech.pdf | 10.21437/Interspeech.2024-404 | 262-266 | @inproceedings{wang24k_interspeech,
title = {{Towards Effective and Efficient Non-autoregressive Decoding Using Block-based Attention Mask}},
author = {Tianzi Wang and Xurong Xie and Zhaoqing Li and Shoukang Hu and Zengrui Jin and Jiajun Deng and Mingyu Cui and Shujie Hu and Mengzhe Geng and Guinan Li and He... | This paper proposes a novel non-autoregressive (NAR) block-based Attention Mask Decoder (AMD) that flexibly balances performance-efficiency trade-offs for Conformer ASR systems. AMD performs parallel NAR inference within contiguous blocks of output labels that are concealed using attention masks, while conducting left-... | 2406.10034 | title_snapshot |
zou24_interspeech | E-Paraformer: A Faster and Better Parallel Transformer for Non-autoregressive End-to-End Mandarin Speech Recognition | [
"Kun Zou",
"Fengyun Tan",
"Ziyang Zhuang",
"Chenfeng Miao",
"Tao Wei",
"Shaodan Zhai",
"Zijian Li",
"Wei Hu",
"Shaojun Wang",
"Jing Xiao"
] | https://www.isca-archive.org/interspeech_2024/zou24_interspeech.html | https://www.isca-archive.org/interspeech_2024/zou24_interspeech.pdf | 10.21437/Interspeech.2024-1891 | 267-271 | @inproceedings{zou24_interspeech,
title = {{E-Paraformer: A Faster and Better Parallel Transformer for Non-autoregressive End-to-End Mandarin Speech Recognition}},
author = {Kun Zou and Fengyun Tan and Ziyang Zhuang and Chenfeng Miao and Tao Wei and Shaodan Zhai and Zijian Li and Wei Hu and Shaojun Wang and... | Paraformer is a powerful non-autoregressive (NAR) model for Mandarin speech recognition. It relies on Continuous Integrate-and-Fire (CIF) to implement parallel decoding. However, the CIF mechanism needs to recursively obtain the acoustic boundary of the emitted token, which will lead to inefficiency. In this paper, we ... | null | null |
ciaperoni24_interspeech | Beam-search SIEVE for low-memory speech recognition | [
"Martino Ciaperoni",
"Athanasios Katsamanis",
"Aristides Gionis",
"Panagiotis Karras"
] | https://www.isca-archive.org/interspeech_2024/ciaperoni24_interspeech.html | https://www.isca-archive.org/interspeech_2024/ciaperoni24_interspeech.pdf | 10.21437/Interspeech.2024-2457 | 272-276 | @inproceedings{ciaperoni24_interspeech,
title = {{Beam-search SIEVE for low-memory speech recognition}},
author = {Martino Ciaperoni and Athanasios Katsamanis and Aristides Gionis and Panagiotis Karras},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {272--276},
doi = {10.214... | A capacity to recognize speech offline eliminates privacy concerns and the need for an internet connection. Despite efforts to reduce the memory demands of speech recognition systems, these demands remain formidable and thus popular tools such as Kaldi run best via cloud computing. The key bottleneck arises form the fa... | null | null |
galvez24_interspeech | Speed of Light Exact Greedy Decoding for RNN-T Speech Recognition Models on GPU | [
"Daniel Galvez",
"Vladimir Bataev",
"Hainan Xu",
"Tim Kaldewey"
] | https://www.isca-archive.org/interspeech_2024/galvez24_interspeech.html | https://www.isca-archive.org/interspeech_2024/galvez24_interspeech.pdf | 10.21437/Interspeech.2024-1591 | 277-281 | @inproceedings{galvez24_interspeech,
title = {{Speed of Light Exact Greedy Decoding for RNN-T Speech Recognition Models on GPU}},
author = {Daniel Galvez and Vladimir Bataev and Hainan Xu and Tim Kaldewey},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {277--281},
doi = {10.... | The vast majority of inference time for RNN Transducer (RNN-T) models today is spent on decoding. Current state-of-the-art RNN-T decoding implementations leave the GPU idle 80% of the time. Leveraging a new CUDA 12.4 feature, CUDA graph conditional nodes, we present an exact GPU-based implementation of greedy decoding ... | 2406.03791 | title_snapshot |
wang24w_interspeech | Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm | [
"Weiran Wang",
"Zelin Wu",
"Diamantino Caseiro",
"Tsendsuren Munkhdalai",
"Khe Chai Sim",
"Pat Rondon",
"Golan Pundak",
"Gan Song",
"Rohit Prabhavalkar",
"Zhong Meng",
"Ding Zhao",
"Tara Sainath",
"Yanzhang He",
"Pedro Moreno Mengibar"
] | https://www.isca-archive.org/interspeech_2024/wang24w_interspeech.html | https://www.isca-archive.org/interspeech_2024/wang24w_interspeech.pdf | 10.21437/Interspeech.2024-1349 | 282-286 | @inproceedings{wang24w_interspeech,
title = {{Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm}},
author = {Weiran Wang and Zelin Wu and Diamantino Caseiro and Tsendsuren Munkhdalai and Khe Chai Sim and Pat Rondon and Golan Pundak and Gan Song and Rohit Prabhavalkar and Zhong Meng and Ding Z... | We propose a GPU/TPU-friendly implementation for contextual biasing based on the Knuth-Morris-Pratt (KMP) pattern matching algorithm. Our algorithms simulate classical search-based biasing approaches which are often implemented in the weighted finite state transducer (WFST) framework, with careful considerations on mem... | 2310.00178 | title_snapshot |
takagi24_interspeech | Text-only Domain Adaptation for CTC-based Speech Recognition through Substitution of Implicit Linguistic Information in the Search Space | [
"Tatsunari Takagi",
"Yukoh Wakabayashi",
"Atsunori Ogawa",
"Norihide Kitaoka"
] | https://www.isca-archive.org/interspeech_2024/takagi24_interspeech.html | https://www.isca-archive.org/interspeech_2024/takagi24_interspeech.pdf | 10.21437/Interspeech.2024-2222 | 287-291 | @inproceedings{takagi24_interspeech,
title = {{Text-only Domain Adaptation for CTC-based Speech Recognition through Substitution of Implicit Linguistic Information in the Search Space}},
author = {Tatsunari Takagi and Yukoh Wakabayashi and Atsunori Ogawa and Norihide Kitaoka},
year = {2024},
booktit... | Domain adaptation using only language models in Automatic Speech Recognition (ASR) has been widely studied because of its practicality. Still, it remains challenging for non-autoregressive ASR models such as Connectionist Temporal Classification (CTC)-based ones. Against this background, this study addresses a text-onl... | null | null |
wang24la_interspeech | Pitch-Aware RNN-T for Mandarin Chinese Mispronunciation Detection and Diagnosis | [
"Xintong Wang",
"Mingqian Shi",
"Ye Wang"
] | https://www.isca-archive.org/interspeech_2024/wang24la_interspeech.html | https://www.isca-archive.org/interspeech_2024/wang24la_interspeech.pdf | 10.21437/Interspeech.2024-2297 | 292-296 | @inproceedings{wang24la_interspeech,
title = {{Pitch-Aware RNN-T for Mandarin Chinese Mispronunciation Detection and Diagnosis}},
author = {Xintong Wang and Mingqian Shi and Ye Wang},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {292--296},
doi = {10.21437/Interspeech.2024-... | Mispronunciation Detection and Diagnosis (MDD) systems, leveraging Automatic Speech Recognition (ASR), face two main challenges in Mandarin Chinese: 1) The two-stage models create an information gap between the phoneme or tone classification stage and the MDD stage. 2) The scarcity of Mandarin MDD datasets limits model... | 2406.04595 | title_snapshot |
chen24c_interspeech | MultiPA: A Multi-task Speech Pronunciation Assessment Model for Open Response Scenarios | [
"Yu-Wen Chen",
"Zhou Yu",
"Julia Hirschberg"
] | https://www.isca-archive.org/interspeech_2024/chen24c_interspeech.html | https://www.isca-archive.org/interspeech_2024/chen24c_interspeech.pdf | 10.21437/Interspeech.2024-123 | 297-301 | @inproceedings{chen24c_interspeech,
title = {{MultiPA: A Multi-task Speech Pronunciation Assessment Model for Open Response Scenarios}},
author = {Yu-Wen Chen and Zhou Yu and Julia Hirschberg},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {297--301},
doi = {10.21437/Intersp... | Pronunciation assessment models designed for open response scenarios enable users to practice language skills in a manner similar to real-life communication. However, previous open-response pronunciation assessment models have predominantly focused on a single pronunciation task, such as sentence-level accuracy, rather... | 2308.12490 | title_snapshot |
cao24b_interspeech | A Framework for Phoneme-Level Pronunciation Assessment Using CTC | [
"Xinwei Cao",
"Zijian Fan",
"Torbjørn Svendsen",
"Giampiero Salvi"
] | https://www.isca-archive.org/interspeech_2024/cao24b_interspeech.html | https://www.isca-archive.org/interspeech_2024/cao24b_interspeech.pdf | 10.21437/Interspeech.2024-459 | 302-306 | @inproceedings{cao24b_interspeech,
title = {{A Framework for Phoneme-Level Pronunciation Assessment Using CTC}},
author = {Xinwei Cao and Zijian Fan and Torbjørn Svendsen and Giampiero Salvi},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {302--306},
doi = {10.21437/Intersp... | Traditional phoneme-level goodness of pronunciation (GOP) methods require phoneme to speech alignment. The drawback is that these methods, by their definitions, are prone to alignment errors and preclude the possibility of deletion and insertion errors in pronunciation. We produce experimental evidence that CTC-based m... | null | null |
shahin24_interspeech | Phonological-Level Mispronunciation Detection and Diagnosis | [
"Mostafa Shahin",
"Beena Ahmed"
] | https://www.isca-archive.org/interspeech_2024/shahin24_interspeech.html | https://www.isca-archive.org/interspeech_2024/shahin24_interspeech.pdf | 10.21437/Interspeech.2024-2217 | 307-311 | @inproceedings{shahin24_interspeech,
title = {{Phonological-Level Mispronunciation Detection and Diagnosis}},
author = {Mostafa Shahin and Beena Ahmed},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {307--311},
doi = {10.21437/Interspeech.2024-2217},
issn = {2958-1796... | The automatic identification and analysis of pronunciation errors, known as mispronunciation detection and diagnosis (MDD), is vital in computer-aided pronunciation learning (CAPL) tools for second-language (L2) learning. Existing MDD methods focus on analyzing phonemes, but they can only detect categorical errors for ... | 2311.07037 | title_judge |
do24_interspeech | Acoustic Feature Mixup for Balanced Multi-aspect Pronunciation Assessment | [
"Heejin Do",
"Wonjun Lee",
"Gary Geunbae Lee"
] | https://www.isca-archive.org/interspeech_2024/do24_interspeech.html | https://www.isca-archive.org/interspeech_2024/do24_interspeech.pdf | 10.21437/Interspeech.2024-2498 | 312-316 | @inproceedings{do24_interspeech,
title = {{Acoustic Feature Mixup for Balanced Multi-aspect Pronunciation Assessment}},
author = {Heejin Do and Wonjun Lee and Gary Geunbae Lee},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {312--316},
doi = {10.21437/Interspeech.2024-2498},... | In automated pronunciation assessment, recent emphasis progressively lies on evaluating multiple aspects to provide enriched feedback. However, acquiring multi-aspect-score labeled data for non-native language learners' speech poses challenges; moreover, it often leads to score-imbalanced distributions. In this paper, ... | 2406.15723 | title_snapshot |
phan24_interspeech | Automated content assessment and feedback for Finnish L2 learners in a picture description speaking task | [
"Nhan Phan",
"Anna von Zansen",
"Maria Kautonen",
"Ekaterina Voskoboinik",
"Tamas Grosz",
"Raili Hilden",
"Mikko Kurimo"
] | https://www.isca-archive.org/interspeech_2024/phan24_interspeech.html | https://www.isca-archive.org/interspeech_2024/phan24_interspeech.pdf | 10.21437/Interspeech.2024-1166 | 317-321 | @inproceedings{phan24_interspeech,
title = {{Automated content assessment and feedback for Finnish L2 learners in a picture description speaking task}},
author = {Nhan Phan and Anna {von Zansen} and Maria Kautonen and Ekaterina Voskoboinik and Tamas Grosz and Raili Hilden and Mikko Kurimo},
year = {20... | We propose a framework to address several unsolved challenges in second language (L2) automatic speaking assessment (ASA) and feedback. The challenges include: 1. ASA of visual task completion, 2. automated content grading and explanation of spontaneous L2 speech, 3. corrective feedback generation for L2 learners, and ... | null | null |
wang24c_interspeech | Query-by-Example Keyword Spotting Using Spectral-Temporal Graph Attentive Pooling and Multi-Task Learning | [
"Zhenyu Wang",
"Shuyu Kong",
"Li Wan",
"Biqiao Zhang",
"Yiteng Huang",
"Mumin Jin",
"Ming Sun",
"Xin Lei",
"Zhaojun Yang"
] | https://www.isca-archive.org/interspeech_2024/wang24c_interspeech.html | https://www.isca-archive.org/interspeech_2024/wang24c_interspeech.pdf | 10.21437/Interspeech.2024-100 | 322-326 | @inproceedings{wang24c_interspeech,
title = {{Query-by-Example Keyword Spotting Using Spectral-Temporal Graph Attentive Pooling and Multi-Task Learning}},
author = {Zhenyu Wang and Shuyu Kong and Li Wan and Biqiao Zhang and Yiteng Huang and Mumin Jin and Ming Sun and Xin Lei and Zhaojun Yang},
year = ... | Existing keyword spotting (KWS) systems primarily rely on predefined keyword phrases. However, the ability to recognize customized keywords is crucial for tailoring interactions with intelligent devices. In this paper, we present a novel Query-by-Example (QbyE) KWS system that employs spectral-temporal graph attentive ... | 2409.00099 | title_snapshot |
jung24_interspeech | Relational Proxy Loss for Audio-Text based Keyword Spotting | [
"Youngmoon Jung",
"Seungjin Lee",
"Joon-Young Yang",
"Jaeyoung Roh",
"Chang Woo Han",
"Hoon-Young Cho"
] | https://www.isca-archive.org/interspeech_2024/jung24_interspeech.html | https://www.isca-archive.org/interspeech_2024/jung24_interspeech.pdf | 10.21437/Interspeech.2024-229 | 327-331 | @inproceedings{jung24_interspeech,
title = {{Relational Proxy Loss for Audio-Text based Keyword Spotting}},
author = {Youngmoon Jung and Seungjin Lee and Joon-Young Yang and Jaeyoung Roh and Chang Woo Han and Hoon-Young Cho},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {327--331},... | In recent years, there has been an increasing focus on user convenience, leading to increased interest in text-based keyword enrollment systems for keyword spotting (KWS). Since the system utilizes text input during the enrollment phase and audio input during actual usage, we call this task audio-text based KWS. To ena... | 2406.05314 | title_snapshot |
jin24d_interspeech | CTC-aligned Audio-Text Embedding for Streaming Open-vocabulary Keyword Spotting | [
"Sichen Jin",
"Youngmoon Jung",
"Seungjin Lee",
"Jaeyoung Roh",
"Changwoo Han",
"Hoonyoung Cho"
] | https://www.isca-archive.org/interspeech_2024/jin24d_interspeech.html | https://www.isca-archive.org/interspeech_2024/jin24d_interspeech.pdf | 10.21437/Interspeech.2024-706 | 332-336 | @inproceedings{jin24d_interspeech,
title = {{CTC-aligned Audio-Text Embedding for Streaming Open-vocabulary Keyword Spotting}},
author = {Sichen Jin and Youngmoon Jung and Seungjin Lee and Jaeyoung Roh and Changwoo Han and Hoonyoung Cho},
year = {2024},
booktitle = {{Interspeech 2024}},
pages ... | This paper introduces a novel approach for streaming open-vocabulary keyword spotting (KWS) with text-based keyword enrollment. For every input frame, the proposed method finds the optimal alignment ending at the frame using connectionist temporal classification (CTC) and aggregates the frame-level acoustic embedding (... | 2406.07923 | title_snapshot |
li24r_interspeech | Text-aware Speech Separation for Multi-talker Keyword Spotting | [
"Haoyu Li",
"Baochen Yang",
"Yu Xi",
"Linfeng Yu",
"Tian Tan",
"Hao Li",
"Kai Yu"
] | https://www.isca-archive.org/interspeech_2024/li24r_interspeech.html | https://www.isca-archive.org/interspeech_2024/li24r_interspeech.pdf | 10.21437/Interspeech.2024-789 | 337-341 | @inproceedings{li24r_interspeech,
title = {{Text-aware Speech Separation for Multi-talker Keyword Spotting}},
author = {Haoyu Li and Baochen Yang and Yu Xi and Linfeng Yu and Tian Tan and Hao Li and Kai Yu},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {337--341},
doi = {10... | For noisy environments, ensuring the robustness of keyword spotting (KWS) systems is essential. While much research has focused on noisy KWS, less attention has been paid to multi-talker mixed speech scenarios. Unlike the usual cocktail party problem where multi-talker speech is separated using speaker clues, the key c... | 2406.12447 | title_snapshot |
yen24_interspeech | Language-Universal Speech Attributes Modeling for Zero-Shot Multilingual Spoken Keyword Recognition | [
"Hao Yen",
"Pin-Jui Ku",
"Sabato Marco Siniscalchi",
"Chin-Hui Lee"
] | https://www.isca-archive.org/interspeech_2024/yen24_interspeech.html | https://www.isca-archive.org/interspeech_2024/yen24_interspeech.pdf | 10.21437/Interspeech.2024-1342 | 342-346 | @inproceedings{yen24_interspeech,
title = {{Language-Universal Speech Attributes Modeling for Zero-Shot Multilingual Spoken Keyword Recognition}},
author = {Hao Yen and Pin-Jui Ku and Sabato Marco Siniscalchi and Chin-Hui Lee},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {342--346... | We propose a novel language-universal approach to end-to-end automatic spoken keyword recognition (SKR) leveraging upon (i) a self-supervised pre-trained model, and (ii) a set of universal speech attributes (manner and place of articulation). Specifically, Wav2Vec2.0 is used to generate robust speech representations, f... | 2406.02488 | title_snapshot |
monteiro24_interspeech | Adding User Feedback To Enhance CB-Whisper | [
"Raul Monteiro"
] | https://www.isca-archive.org/interspeech_2024/monteiro24_interspeech.html | https://www.isca-archive.org/interspeech_2024/monteiro24_interspeech.pdf | 10.21437/Interspeech.2024-1664 | 347-351 | @inproceedings{monteiro24_interspeech,
title = {{Adding User Feedback To Enhance CB-Whisper}},
author = {Raul Monteiro},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {347--351},
doi = {10.21437/Interspeech.2024-1664},
issn = {2958-1796},
} | Contextual biasing has been demonstrated to be effective in improving Whisper recall for named entities or domain-specific words. In a recent work, CB-Whisper takes an additional step and integrates a classifier for open-vocabulary keyword-spotting (OV-KWS) to retrieve keywords from an external database to form a restr... | null | null |
peng24b_interspeech | OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer | [
"Yifan Peng",
"Jinchuan Tian",
"William Chen",
"Siddhant Arora",
"Brian Yan",
"Yui Sudo",
"Muhammad Shakeel",
"Kwanghee Choi",
"Jiatong Shi",
"Xuankai Chang",
"Jee-weon Jung",
"Shinji Watanabe"
] | https://www.isca-archive.org/interspeech_2024/peng24b_interspeech.html | https://www.isca-archive.org/interspeech_2024/peng24b_interspeech.pdf | 10.21437/Interspeech.2024-1194 | 352-356 | @inproceedings{peng24b_interspeech,
title = {{OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer}},
author = {Yifan Peng and Jinchuan Tian and William Chen and Siddhant Arora and Brian Yan and Yui Sudo and Muhammad Shakeel and Kwanghee Choi and Jiatong Shi and Xuankai Chang... | Recent studies have highlighted the importance of fully open foundation models. The Open Whisper-style Speech Model (OWSM) is an initial step towards reproducing OpenAI Whisper using public data and open-source toolkits. However, previous versions of OWSM (v1 to v3) are still based on standard Transformer, which might ... | 2401.16658 | title_snapshot |
chen24m_interspeech | Parameter-Efficient Adapter Based on Pre-trained Models for Speech Translation | [
"Nan Chen",
"Yonghe Wang",
"Feilong Bao"
] | https://www.isca-archive.org/interspeech_2024/chen24m_interspeech.html | https://www.isca-archive.org/interspeech_2024/chen24m_interspeech.pdf | 10.21437/Interspeech.2024-759 | 357-361 | @inproceedings{chen24m_interspeech,
title = {{Parameter-Efficient Adapter Based on Pre-trained Models for Speech Translation}},
author = {Nan Chen and Yonghe Wang and Feilong Bao},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {357--361},
doi = {10.21437/Interspeech.2024-759... | Multi-task learning (MTL) approach leverages pre-trained models in speech and machine translation and has significantly advanced speech-to-text translation tasks. However, it introduces a considerable number of parameters, leading to increasing training costs. Most parameter-efficient fine-tuning (PEFT) methods only tr... | null | null |
abdullah24_interspeech | Wave to Interlingua: Analyzing Representations of Multilingual Speech Transformers for Spoken Language Translation | [
"Badr M. Abdullah",
"Mohammed Maqsood Shaik",
"Dietrich Klakow"
] | https://www.isca-archive.org/interspeech_2024/abdullah24_interspeech.html | https://www.isca-archive.org/interspeech_2024/abdullah24_interspeech.pdf | 10.21437/Interspeech.2024-2109 | 362-366 | @inproceedings{abdullah24_interspeech,
title = {{Wave to Interlingua: Analyzing Representations of Multilingual Speech Transformers for Spoken Language Translation}},
author = {Badr M. Abdullah and Mohammed Maqsood Shaik and Dietrich Klakow},
year = {2024},
booktitle = {{Interspeech 2024}},
pages ... | In Transformer-based Speech-to-Text (S2T) translation, an encoder-decoder model is trained end-to-end to take as input an untranscribed acoustic signal in the source language and directly generate a text translation in the target language. S2T translation models can also be trained in multilingual setups where a sing... | null | null |
chen24w_interspeech | Knowledge-Preserving Pluggable Modules for Multilingual Speech Translation Tasks | [
"Nan Chen",
"Yonghe Wang",
"Feilong Bao"
] | https://www.isca-archive.org/interspeech_2024/chen24w_interspeech.html | https://www.isca-archive.org/interspeech_2024/chen24w_interspeech.pdf | 10.21437/Interspeech.2024-2346 | 367-371 | @inproceedings{chen24w_interspeech,
title = {{Knowledge-Preserving Pluggable Modules for Multilingual Speech Translation Tasks}},
author = {Nan Chen and Yonghe Wang and Feilong Bao},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {367--371},
doi = {10.21437/Interspeech.2024-2... | Multilingual speech translation tasks typically employ retraining, regularization, or resampling methods to add new languages. Retraining the model significantly increases training time and cost. Moreover, using existing regularization or resampling methods to balance performance between new and original languages migh... | null | null |
rabatin24_interspeech | Navigating the Minefield of MT Beam Search in Cascaded Streaming Speech Translation | [
"Rastislav Rabatin",
"Frank Seide",
"Ernie Chang"
] | https://www.isca-archive.org/interspeech_2024/rabatin24_interspeech.html | https://www.isca-archive.org/interspeech_2024/rabatin24_interspeech.pdf | 10.21437/Interspeech.2024-1759 | 372-376 | @inproceedings{rabatin24_interspeech,
title = {{Navigating the Minefield of MT Beam Search in Cascaded Streaming Speech Translation}},
author = {Rastislav Rabatin and Frank Seide and Ernie Chang},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {372--376},
doi = {10.21437/Inte... | We adapt the well-known beam-search algorithm for machine translation to operate in a cascaded real-time speech translation system. This proved to be more complex than initially anticipated, due to four key challenges: (1) real-time processing of intermediate and final transcriptions with incomplete words from ASR, (2)... | 2407.11010 | title_snapshot |
wang24aa_interspeech | Soft Language Identification for Language-Agnostic Many-to-One End-to-End Speech Translation | [
"Peidong Wang",
"Jian Xue",
"Jinyu Li",
"Junkun Chen",
"Aswin Shanmugam Subramanian"
] | https://www.isca-archive.org/interspeech_2024/wang24aa_interspeech.html | https://www.isca-archive.org/interspeech_2024/wang24aa_interspeech.pdf | 10.21437/Interspeech.2024-1507 | 377-381 | @inproceedings{wang24aa_interspeech,
title = {{Soft Language Identification for Language-Agnostic Many-to-One End-to-End Speech Translation}},
author = {Peidong Wang and Jian Xue and Jinyu Li and Junkun Chen and Aswin Shanmugam Subramanian},
year = {2024},
booktitle = {{Interspeech 2024}},
pages ... | Language-agnostic many-to-one end-to-end speech translation models can convert audio signals from different source languages into text in a target language. These models do not need source language identification, which improves user experience. In some cases, the input language can be given or estimated. Our goal is t... | 2406.10276 | title_snapshot |
oneata24_interspeech | Translating speech with just images | [
"Dan Oneata",
"Herman Kamper"
] | https://www.isca-archive.org/interspeech_2024/oneata24_interspeech.html | https://www.isca-archive.org/interspeech_2024/oneata24_interspeech.pdf | 10.21437/Interspeech.2024-903 | 387-391 | @inproceedings{oneata24_interspeech,
title = {{Translating speech with just images}},
author = {Dan Oneata and Herman Kamper},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {387--391},
doi = {10.21437/Interspeech.2024-903},
issn = {2958-1796},
} | Visually grounded speech models link speech to images. We extend this connection by linking images to text via an existing image captioning system, and as a result gain the ability to map speech audio directly to text. This approach can be used for speech translation with just images by having the audio in a different ... | 2406.07133 | title_snapshot |
khurana24_interspeech | ZeroST: Zero-Shot Speech Translation | [
"Sameer Khurana",
"Chiori Hori",
"Antoine Laurent",
"Gordon Wichern",
"Jonathan Le Roux"
] | https://www.isca-archive.org/interspeech_2024/khurana24_interspeech.html | https://www.isca-archive.org/interspeech_2024/khurana24_interspeech.pdf | 10.21437/Interspeech.2024-1088 | 392-396 | @inproceedings{khurana24_interspeech,
title = {{ZeroST: Zero-Shot Speech Translation}},
author = {Sameer Khurana and Chiori Hori and Antoine Laurent and Gordon Wichern and Jonathan {Le Roux}},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {392--396},
doi = {10.21437/Interspe... | Our work introduces the Zero-Shot Speech Translation (ZeroST) framework, leveraging the synergistic potential of pre trained multilingual speech and text foundation models. Inspired by recent advances in multimodal foundation models, ZeroST utilizes a Query Transformer (Q-Former) to seamlessly connect a speech foundati... | null | null |
li24ca_interspeech | A multimodal approach to study the nature of coordinative patterns underlying speech rhythm | [
"Jinyu Li",
"Leonardo Lancia"
] | https://www.isca-archive.org/interspeech_2024/li24ca_interspeech.html | https://www.isca-archive.org/interspeech_2024/li24ca_interspeech.pdf | 10.21437/Interspeech.2024-1571 | 397-401 | @inproceedings{li24ca_interspeech,
title = {{A multimodal approach to study the nature of coordinative patterns underlying speech rhythm}},
author = {Jinyu Li and Leonardo Lancia},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {397--401},
doi = {10.21437/Interspeech.2024-157... | Research on speech rhythm suggests that coordination between syllable and supra-syllabic prominence defines rhythmic differences between languages. This study investigates the role of language-specific phonological processes in the emergence of language-specific coordinative patterns underlying speech rhythm, which res... | null | null |
wu24k_interspeech | Towards EMG-to-Speech with Necklace Form Factor | [
"Peter Wu",
"Ryan Kaveh",
"Raghav Nautiyal",
"Christine Zhang",
"Albert Guo",
"Anvitha Kachinthaya",
"Tavish Mishra",
"Bohan Yu",
"Alan W Black",
"Rikky Muller",
"Gopala Krishna Anumanchipalli"
] | https://www.isca-archive.org/interspeech_2024/wu24k_interspeech.html | https://www.isca-archive.org/interspeech_2024/wu24k_interspeech.pdf | 10.21437/Interspeech.2024-1568 | 402-406 | @inproceedings{wu24k_interspeech,
title = {{Towards EMG-to-Speech with Necklace Form Factor}},
author = {Peter Wu and Ryan Kaveh and Raghav Nautiyal and Christine Zhang and Albert Guo and Anvitha Kachinthaya and Tavish Mishra and Bohan Yu and Alan W Black and Rikky Muller and Gopala Krishna Anumanchipalli},
... | Electrodes for decoding speech from electromyography (EMG) are typically placed on the face, requiring adhesives that are inconvenient and skin-irritating if used regularly. We explore a different device form factor, where dry electrodes are placed around the neck instead. 11-word, multi-speaker voiced EMG classifiers ... | 2407.21345 | title_judge |
bras24_interspeech | Using articulated speech EEG signals for imagined speech decoding | [
"Chris Bras",
"Tanvina Patel",
"Odette Scharenborg"
] | https://www.isca-archive.org/interspeech_2024/bras24_interspeech.html | https://www.isca-archive.org/interspeech_2024/bras24_interspeech.pdf | 10.21437/Interspeech.2024-1289 | 407-411 | @inproceedings{bras24_interspeech,
title = {{Using articulated speech EEG signals for imagined speech decoding}},
author = {Chris Bras and Tanvina Patel and Odette Scharenborg},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {407--411},
doi = {10.21437/Interspeech.2024-1289},... | Brain-Computer Interfaces (BCIs) open avenues for communication among individuals unable to use voice or gestures. Silent speech interfaces are one such approach for BCIs that could offer a transformative means of connecting with the external world. Performance on imagined speech decoding however is rather low due to, ... | null | null |
kwon24_interspeech | Direct Speech Synthesis from Non-Invasive, Neuromagnetic Signals | [
"Jinuk Kwon",
"David Harwath",
"Debadatta Dash",
"Paul Ferrari",
"Jun Wang"
] | https://www.isca-archive.org/interspeech_2024/kwon24_interspeech.html | https://www.isca-archive.org/interspeech_2024/kwon24_interspeech.pdf | 10.21437/Interspeech.2024-2153 | 412-416 | @inproceedings{kwon24_interspeech,
title = {{Direct Speech Synthesis from Non-Invasive, Neuromagnetic Signals}},
author = {Jinuk Kwon and David Harwath and Debadatta Dash and Paul Ferrari and Jun Wang},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {412--416},
doi = {10.2143... | Direct speech synthesis from neural activity can enable individuals to communicate without articulatory movement or vocalization. A number of recent speech braincomputer interface (BCI) studies have been conducted using invasive neuroimaging techniques, which require neurosurgery to implant electrodes in the brain. In ... | null | null |
yang24o_interspeech | Optical Flow Guided Tongue Trajectory Generation for Diffusion-based Acoustic to Articulatory Inversion | [
"Yudong Yang",
"Rongfeng Su",
"Rukiye Ruzi",
"Manwa Ng",
"Shaofeng Zhao",
"Nan Yan",
"Lan Wang"
] | https://www.isca-archive.org/interspeech_2024/yang24o_interspeech.html | https://www.isca-archive.org/interspeech_2024/yang24o_interspeech.pdf | 10.21437/Interspeech.2024-1864 | 417-421 | @inproceedings{yang24o_interspeech,
title = {{Optical Flow Guided Tongue Trajectory Generation for Diffusion-based Acoustic to Articulatory Inversion}},
author = {Yudong Yang and Rongfeng Su and Rukiye Ruzi and Manwa Ng and Shaofeng Zhao and Nan Yan and Lan Wang},
year = {2024},
booktitle = {{Inters... | The diffusion-based Acoustic-to-Articulatory Inversion (AAI) approach has been shown impressive results for converting audio into Ultrasound Tongue Imaging (UTI) data with clear tongue contours. However, Mean Square Error (MSE) based diffusion models focus on the pixel error between reference and generated UTI data, in... | null | null |
jain24_interspeech | Multimodal Segmentation for Vocal Tract Modeling | [
"Rishi Jain",
"Bohan Yu",
"Peter Wu",
"Tejas Prabhune",
"Gopala Anumanchipalli"
] | https://www.isca-archive.org/interspeech_2024/jain24_interspeech.html | https://www.isca-archive.org/interspeech_2024/jain24_interspeech.pdf | 10.21437/Interspeech.2024-2223 | 422-426 | @inproceedings{jain24_interspeech,
title = {{Multimodal Segmentation for Vocal Tract Modeling}},
author = {Rishi Jain and Bohan Yu and Peter Wu and Tejas Prabhune and Gopala Anumanchipalli},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {422--426},
doi = {10.21437/Interspeec... | Accurate modeling of the vocal tract is necessary to construct articulatory representations for interpretable speech processing and linguistics. However, vocal tract modeling is challenging because many internal articulators are occluded from external motion capture technologies. Real-time magnetic resonance imaging (R... | 2406.15754 | title_snapshot |
bandekar24_interspeech | Articulatory synthesis using representations learnt through phonetic label-aware contrastive loss | [
"Jesuraj Bandekar",
"Sathvik Udupa",
"Prasanta Kumar Ghosh"
] | https://www.isca-archive.org/interspeech_2024/bandekar24_interspeech.html | https://www.isca-archive.org/interspeech_2024/bandekar24_interspeech.pdf | 10.21437/Interspeech.2024-1756 | 427-431 | @inproceedings{bandekar24_interspeech,
title = {{Articulatory synthesis using representations learnt through phonetic label-aware contrastive loss}},
author = {Jesuraj Bandekar and Sathvik Udupa and Prasanta Kumar Ghosh},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {427--431},
d... | Articulatory speech synthesis is a challenging task which requires mapping of time-varying articulatory trajectories and speech. In recent years, deep learning methods have been proposed for speech synthesis which have achieved significant progress towards human-like speech generation. However, articulatory speech synt... | null | null |
yan24b_interspeech | Auditory Attention Decoding in Four-Talker Environment with EEG | [
"Yujie Yan",
"Xiran Xu",
"Haolin Zhu",
"Pei Tian",
"Zhongshu Ge",
"Xihong Wu",
"Jing Chen"
] | https://www.isca-archive.org/interspeech_2024/yan24b_interspeech.html | https://www.isca-archive.org/interspeech_2024/yan24b_interspeech.pdf | 10.21437/Interspeech.2024-739 | 432-436 | @inproceedings{yan24b_interspeech,
title = {{Auditory Attention Decoding in Four-Talker Environment with EEG}},
author = {Yujie Yan and Xiran Xu and Haolin Zhu and Pei Tian and Zhongshu Ge and Xihong Wu and Jing Chen},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {432--436},
doi ... | Auditory Attention Decoding (AAD) is a technique that determines the focus of a listener's attention in complex auditory scenes according to cortical neural responses. Existing research largely examines two-talker scenarios, insufficient for real-world complexity. This study introduced a new AAD database for a four-tal... | null | null |
lin24f_interspeech | ASA: An Auditory Spatial Attention Dataset with Multiple Speaking Locations | [
"Zijie Lin",
"Tianyu He",
"Siqi Cai",
"Haizhou Li"
] | https://www.isca-archive.org/interspeech_2024/lin24f_interspeech.html | https://www.isca-archive.org/interspeech_2024/lin24f_interspeech.pdf | 10.21437/Interspeech.2024-753 | 437-441 | @inproceedings{lin24f_interspeech,
title = {{ASA: An Auditory Spatial Attention Dataset with Multiple Speaking Locations}},
author = {Zijie Lin and Tianyu He and Siqi Cai and Haizhou Li},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {437--441},
doi = {10.21437/Interspeech.2... | Recent studies have demonstrated the feasibility of localizing an attended sound source from electroencephalography (EEG) signals in a cocktail party scenario. This is referred to as EEG-enabled Auditory Spatial Attention Detection (ASAD). Despite the promise, there is a lack of ASAD datasets. Most existing ASAD datase... | null | null |
pahuja24_interspeech | Leveraging Graphic and Convolutional Neural Networks for Auditory Attention Detection with EEG | [
"Saurav Pahuja",
"Gabriel Ivucic",
"Pascal Himmelmann",
"Siqi Cai",
"Tanja Schultz",
"Haizhou Li"
] | https://www.isca-archive.org/interspeech_2024/pahuja24_interspeech.html | https://www.isca-archive.org/interspeech_2024/pahuja24_interspeech.pdf | 10.21437/Interspeech.2024-1217 | 442-446 | @inproceedings{pahuja24_interspeech,
title = {{Leveraging Graphic and Convolutional Neural Networks for Auditory Attention Detection with EEG}},
author = {Saurav Pahuja and Gabriel Ivucic and Pascal Himmelmann and Siqi Cai and Tanja Schultz and Haizhou Li},
year = {2024},
booktitle = {{Interspeech 2... | Recent work has shown that the locus of selective auditory attention in multi-speaker settings can be decoded from single-trial electroencephalography (EEG). This study represents the first effort to investigate the decoding of selective auditory attention through the utilization of an ensemble model. Specifically, we ... | null | null |
pistor24_interspeech | Echoes of Implicit Bias Exploring Aesthetics and Social Meanings of Swiss German Dialect Features | [
"Tillmann Pistor",
"Adrian Leemann"
] | https://www.isca-archive.org/interspeech_2024/pistor24_interspeech.html | https://www.isca-archive.org/interspeech_2024/pistor24_interspeech.pdf | 10.21437/Interspeech.2024-2013 | 447-451 | @inproceedings{pistor24_interspeech,
title = {{Echoes of Implicit Bias Exploring Aesthetics and Social Meanings of Swiss German Dialect Features}},
author = {Tillmann Pistor and Adrian Leemann},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {447--451},
doi = {10.21437/Inters... | This study investigates the phonaesthetics and perceptual dynamics of Swiss German dialects, focusing on how particular sound features influence subjective assessments and, in doing so, contribute to dialect stereotypes. By examining 24 linguistic features of Bern and Zurich German, including nine vowels and 15 consona... | null | null |
li24ra_interspeech | In search of structure and correspondence in intra-speaker trial-to-trial variability | [
"Vivian G. Li"
] | https://www.isca-archive.org/interspeech_2024/li24ra_interspeech.html | https://www.isca-archive.org/interspeech_2024/li24ra_interspeech.pdf | 10.21437/Interspeech.2024-2456 | 452-456 | @inproceedings{li24ra_interspeech,
title = {{In search of structure and correspondence in intra-speaker trial-to-trial variability}},
author = {Vivian G. Li},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {452--456},
doi = {10.21437/Interspeech.2024-2456},
issn = {295... | Intra-speaker variability is present even when the talker is uttering the same words in the same social and linguistic context. Studies have revealed that such intra-speaker trial-to-trial variability is connected to speech perception and is actively regulated during speech production. However, the relevant parameters ... | null | null |
smith24_interspeech | Modelled Multivariate Overlap: A method for measuring vowel merger | [
"Irene Smith",
"Morgan Sonderegger",
"The Spade Consortium"
] | https://www.isca-archive.org/interspeech_2024/smith24_interspeech.html | https://www.isca-archive.org/interspeech_2024/smith24_interspeech.pdf | 10.21437/Interspeech.2024-2260 | 457-461 | @inproceedings{smith24_interspeech,
title = {{Modelled Multivariate Overlap: A method for measuring vowel merger}},
author = {Irene Smith and Morgan Sonderegger and The {Spade Consortium}},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {457--461},
doi = {10.21437/Interspeech... | This paper introduces a novel method for quantifying vowel overlap. There is a tension in previous work between using multivariate measures, such as those derived from empirical distri- butions, and the ability to control for unbalanced data and extraneous factors, as is possible when using fitted model parameters. The... | 2406.16319 | title_snapshot |
ochi24_interspeech | Entrainment Analysis and Prosody Prediction of Subsequent Interlocutorâs Backchannels in Dialogue | [
"Keiko Ochi",
"Koji Inoue",
"Divesh Lala",
"Tatsuya Kawahara"
] | https://www.isca-archive.org/interspeech_2024/ochi24_interspeech.html | https://www.isca-archive.org/interspeech_2024/ochi24_interspeech.pdf | 10.21437/Interspeech.2024-628 | 462-466 | @inproceedings{ochi24_interspeech,
title = {{Entrainment Analysis and Prosody Prediction of Subsequent Interlocutorâs Backchannels in Dialogue}},
author = {Keiko Ochi and Koji Inoue and Divesh Lala and Tatsuya Kawahara},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {462--466},
... | This study investigates the characteristics of backchannels showing the entrainment to the interlocutorâs speech. The prosodic features of the dialogues of attentive listening are analyzed to describe how the prosody of Japanese backchannels is affected by the preceding interlocutorâs utterance. We adopt a support ... | null | null |
tanner24_interspeech | Exploring the anatomy of articulation rate in spontaneous English speech: relationships between utterance length effects and social factors | [
"James Tanner",
"Morgan Sonderegger",
"Jane Stuart-Smith",
"Tyler Kendall",
"Jeff Mielke",
"Robin Dodsworth",
"Erik Thomas"
] | https://www.isca-archive.org/interspeech_2024/tanner24_interspeech.html | https://www.isca-archive.org/interspeech_2024/tanner24_interspeech.pdf | 10.21437/Interspeech.2024-1154 | 467-471 | @inproceedings{tanner24_interspeech,
title = {{Exploring the anatomy of articulation rate in spontaneous English speech: relationships between utterance length effects and social factors}},
author = {James Tanner and Morgan Sonderegger and Jane Stuart-Smith and Tyler Kendall and Jeff Mielke and Robin Dodswor... | Speech rate has been shown to vary across social categories such as gender, age, and dialect, while also being conditioned by properties of speech planning. The effect of utterance length, where speech rate is faster and less variable for longer utterances, has also been shown to reduce the role of social factors once ... | 2408.06732 | title_snapshot |
taylor24_interspeech | Familiar and Unfamiliar Speaker Identification in Speech and Singing | [
"Katelyn Taylor",
"Amelia Gully",
"Helena Daffern"
] | https://www.isca-archive.org/interspeech_2024/taylor24_interspeech.html | https://www.isca-archive.org/interspeech_2024/taylor24_interspeech.pdf | 10.21437/Interspeech.2024-1763 | 472-476 | @inproceedings{taylor24_interspeech,
title = {{Familiar and Unfamiliar Speaker Identification in Speech and Singing}},
author = {Katelyn Taylor and Amelia Gully and Helena Daffern},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {472--476},
doi = {10.21437/Interspeech.2024-17... | Little research has been conducted to gauge a listenerâs ability to recognise or identify speakers when presented with samples of singing within the field of Forensic Speech Science. Eight friends and two foil speakers were recorded speaking and singing to investigate the effects of speaker familiarity and singing in... | null | null |
parragallego24_interspeech | Cross-transfer Knowledge between Speech and Text Encoders to Evaluate Customer Satisfaction | [
"Luis Felipe Parra-Gallego",
"Tilak Purohit",
"Bogdan Vlasenko",
"Juan Rafael Orozco-Arroyave",
"Mathew Magimai.-Doss"
] | https://www.isca-archive.org/interspeech_2024/parragallego24_interspeech.html | https://www.isca-archive.org/interspeech_2024/parragallego24_interspeech.pdf | 10.21437/Interspeech.2024-514 | 477-481 | @inproceedings{parragallego24_interspeech,
title = {{Cross-transfer Knowledge between Speech and Text Encoders to Evaluate Customer Satisfaction}},
author = {Luis Felipe Parra-Gallego and Tilak Purohit and Bogdan Vlasenko and Juan Rafael Orozco-Arroyave and Mathew Magimai.-Doss},
year = {2024},
book... | Customer Satisfaction (CS) in call centers influences customer loyalty and the company's reputation. Traditionally, CS evaluations were conducted manually or with classical machine learning algorithms; however, advancements in deep learning have led to automated systems that evaluate CS using speech and text analyses. ... | null | null |
kodali24_interspeech | Fine-tuning of Pre-trained Models for Classification of Vocal Intensity Category from Speech Signals | [
"Manila Kodali",
"Sudarsana Reddy Kadiri",
"Paavo Alku"
] | https://www.isca-archive.org/interspeech_2024/kodali24_interspeech.html | https://www.isca-archive.org/interspeech_2024/kodali24_interspeech.pdf | 10.21437/Interspeech.2024-2237 | 482-486 | @inproceedings{kodali24_interspeech,
title = {{Fine-tuning of Pre-trained Models for Classification of Vocal Intensity Category from Speech Signals}},
author = {Manila Kodali and Sudarsana Reddy Kadiri and Paavo Alku},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {482--486},
doi ... | Speakers regulate vocal intensity on many occasions for example to be heard over a long distance or to express vocal emotions. Humans can regulate vocal intensity over a wide sound pressure level (SPL) range and therefore speech can be categorized into different vocal intensity categories. Recent machine learning expe... | null | null |
kathan24_interspeech | Real-world PTSD Recognition: A Cross-corpus and Cross-linguistic Evaluation | [
"Alexander Kathan",
"Martin Bürger",
"Andreas Triantafyllopoulos",
"Sabrina Milkus",
"Jonas Hohmann",
"Pauline Muderlak",
"Jürgen Schottdorf",
"Richard Musil",
"Björn Schuller",
"Shahin Amiriparian"
] | https://www.isca-archive.org/interspeech_2024/kathan24_interspeech.html | https://www.isca-archive.org/interspeech_2024/kathan24_interspeech.pdf | 10.21437/Interspeech.2024-493 | 487-491 | @inproceedings{kathan24_interspeech,
title = {{Real-world PTSD Recognition: A Cross-corpus and Cross-linguistic Evaluation}},
author = {Alexander Kathan and Martin Bürger and Andreas Triantafyllopoulos and Sabrina Milkus and Jonas Hohmann and Pauline Muderlak and Jürgen Schottdorf and Richard Musil and BjÃ... | Post-traumatic Stress Disorder (PTSD) is a mental condition that develops as a result of catastrophic events. Triggers for this may include experiences, such as military combat, natural disasters, or sexual abuse, having a great influence on the mental wellbeing. Due to the severity of this condition, early detection a... | null | null |
bhattacharya24_interspeech | Switching Tongues, Sharing Hearts: Identifying the Relationship between Empathy and Code-switching in Speech | [
"Debasmita Bhattacharya",
"Eleanor Lin",
"Run Chen",
"Julia Hirschberg"
] | https://www.isca-archive.org/interspeech_2024/bhattacharya24_interspeech.html | https://www.isca-archive.org/interspeech_2024/bhattacharya24_interspeech.pdf | 10.21437/Interspeech.2024-1224 | 492-496 | @inproceedings{bhattacharya24_interspeech,
title = {{Switching Tongues, Sharing Hearts: Identifying the Relationship between Empathy and Code-switching in Speech}},
author = {Debasmita Bhattacharya and Eleanor Lin and Run Chen and Julia Hirschberg},
year = {2024},
booktitle = {{Interspeech 2024}},
... | Among the many multilingual speakers of the world, code-switching (CSW) is a common linguistic phenomenon. Prior sociolinguistic work has shown that factors such as expressing group identity and solidarity, performing affective function, and reflecting shared experiences are related to CSW prevalence in multilingual sp... | null | null |
rosello24_interspeech | Anti-spoofing Ensembling Model: Dynamic Weight Allocation in Ensemble Models for Improved Voice Biometrics Security | [
"Eros Rosello",
"Angel M. Gomez",
"Iván López-Espejo",
"Antonio M. Peinado",
"Juan M. MartÃn-Doñas"
] | https://www.isca-archive.org/interspeech_2024/rosello24_interspeech.html | https://www.isca-archive.org/interspeech_2024/rosello24_interspeech.pdf | 10.21437/Interspeech.2024-403 | 497-501 | @inproceedings{rosello24_interspeech,
title = {{Anti-spoofing Ensembling Model: Dynamic Weight Allocation in Ensemble Models for Improved Voice Biometrics Security}},
author = {Eros Rosello and Angel M. Gomez and Iván López-Espejo and Antonio M. Peinado and Juan M. MartÃn-Doñas},
year = {2024},
... | This paper proposes an ensembling model as spoofed speech countermeasure, with a particular focus on synthetic voice. Despite the recent advances in speaker verification based on deep neural networks, this technology is still susceptible to various malicious attacks, so that some kind of countermeasures are needed. Whi... | null | null |
zhang24j_interspeech | Spoof Diarization: "What Spoofed When" in Partially Spoofed Audio | [
"Lin Zhang",
"Xin Wang",
"Erica Cooper",
"Mireia Diez",
"Federico Landini",
"Nicholas Evans",
"Junichi Yamagishi"
] | https://www.isca-archive.org/interspeech_2024/zhang24j_interspeech.html | https://www.isca-archive.org/interspeech_2024/zhang24j_interspeech.pdf | 10.21437/Interspeech.2024-1365 | 502-506 | @inproceedings{zhang24j_interspeech,
title = {{Spoof Diarization: "What Spoofed When" in Partially Spoofed Audio}},
author = {Lin Zhang and Xin Wang and Erica Cooper and Mireia Diez and Federico Landini and Nicholas Evans and Junichi Yamagishi},
year = {2024},
booktitle = {{Interspeech 2024}},
pag... | This paper defines Spoof Diarization as a novel task in the Partial Spoof (PS) scenario. It aims to determine what spoofed when, which includes not only locating spoof regions but also clustering them according to different spoofing methods. As a pioneering study in spoof diarization, we focus on defining the task, est... | 2406.07816 | title_snapshot |
wu24b_interspeech | Spoofing Speech Detection by Modeling Local Spectro-Temporal and Long-term Dependency | [
"Haochen Wu",
"Wu Guo",
"Zhentao Zhang",
"Wenting Zhao",
"Shengyu Peng",
"Jie Zhang"
] | https://www.isca-archive.org/interspeech_2024/wu24b_interspeech.html | https://www.isca-archive.org/interspeech_2024/wu24b_interspeech.pdf | 10.21437/Interspeech.2024-251 | 507-511 | @inproceedings{wu24b_interspeech,
title = {{Spoofing Speech Detection by Modeling Local Spectro-Temporal and Long-term Dependency}},
author = {Haochen Wu and Wu Guo and Zhentao Zhang and Wenting Zhao and Shengyu Peng and Jie Zhang},
year = {2024},
booktitle = {{Interspeech 2024}},
pages = {507... | In this work, a dual-branch network is proposed to exploit both local and global information of utterances for spoofing speech detection (SSD). The local artifacts of spoofing speech can reside in specific temporal or spectral regions, which are the primary objectives for SSD systems. We propose a spectro-temporal grap... | null | null |
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