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 36 | title stringlengths 23 172 | authors listlengths 1 20 | isca_url stringlengths 66 87 | pdf_url stringlengths 65 86 | doi stringlengths 27 31 | pages stringlengths 3 9 | bibtex large_stringlengths 290 722 | abstract large_stringlengths 462 1.6k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|
cho22_interspeech | SANE-TTS: Stable And Natural End-to-End Multilingual Text-to-Speech | [
"Hyunjae Cho",
"Wonbin Jung",
"Junhyeok Lee",
"Sang Hoon Woo"
] | https://www.isca-archive.org/interspeech_2022/cho22_interspeech.html | https://www.isca-archive.org/interspeech_2022/cho22_interspeech.pdf | 10.21437/Interspeech.2022-46 | 1-5 | @inproceedings{cho22_interspeech,
title = {{SANE-TTS: Stable And Natural End-to-End Multilingual Text-to-Speech}},
author = {Hyunjae Cho and Wonbin Jung and Junhyeok Lee and Sang Hoon Woo},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {1--5},
doi = {10.21437/Interspeech.202... | In this paper, we present SANE-TTS, a stable and natural end-to-end multilingual TTS model. By the difficulty of obtaining multilingual corpus for given speaker, training multilingual TTS model with monolingual corpora is unavoidable. We introduce speaker regularization loss that improves speech naturalness during cros... | 2206.12132 | title_snapshot |
bae22_interspeech | Enhancement of Pitch Controllability using Timbre-Preserving Pitch Augmentation in FastPitch | [
"Hanbin Bae",
"Young-Sun Joo"
] | https://www.isca-archive.org/interspeech_2022/bae22_interspeech.html | https://www.isca-archive.org/interspeech_2022/bae22_interspeech.pdf | 10.21437/Interspeech.2022-55 | 6-10 | @inproceedings{bae22_interspeech,
title = {{Enhancement of Pitch Controllability using Timbre-Preserving Pitch Augmentation in FastPitch}},
author = {Hanbin Bae and Young-Sun Joo},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {6--10},
doi = {10.21437/Interspeech.2022-55},
... | The recently developed pitch-controllable text-to-speech (TTS) model, i.e. FastPitch, was conditioned for the pitch contours. However, the quality of the synthesized speech degraded considerably for pitch values that deviated significantly from the average pitch; i.e. the ability to control vocal pitch was limited. To ... | 2204.05753 | title_snapshot |
lenglet22_interspeech | Speaking Rate Control of end-to-end TTS Models by Direct Manipulation of the Encoder's Output Embeddings | [
"Martin Lenglet",
"Olivier Perrotin",
"Gérard Bailly"
] | https://www.isca-archive.org/interspeech_2022/lenglet22_interspeech.html | https://www.isca-archive.org/interspeech_2022/lenglet22_interspeech.pdf | 10.21437/Interspeech.2022-759 | 11-15 | @inproceedings{lenglet22_interspeech,
title = {{Speaking Rate Control of end-to-end TTS Models by Direct Manipulation of the Encoder's Output Embeddings}},
author = {Martin Lenglet and Olivier Perrotin and Gérard Bailly},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {11--15},
do... | Since neural Text-To-Speech models have achieved such high standards in terms of naturalness, the main focus of the field has gradually shifted to gaining more control over the expressiveness of the synthetic voices. One of these leverages is the control of the speaking rate that has become harder for a human operator ... | null | null |
ju22_interspeech | TriniTTS: Pitch-controllable End-to-end TTS without External Aligner | [
"Yooncheol Ju",
"Ilhwan Kim",
"Hongsun Yang",
"Ji-Hoon Kim",
"Byeongyeol Kim",
"Soumi Maiti",
"Shinji Watanabe"
] | https://www.isca-archive.org/interspeech_2022/ju22_interspeech.html | https://www.isca-archive.org/interspeech_2022/ju22_interspeech.pdf | 10.21437/Interspeech.2022-925 | 16-20 | @inproceedings{ju22_interspeech,
title = {{TriniTTS: Pitch-controllable End-to-end TTS without External Aligner}},
author = {Yooncheol Ju and Ilhwan Kim and Hongsun Yang and Ji-Hoon Kim and Byeongyeol Kim and Soumi Maiti and Shinji Watanabe},
year = {2022},
booktitle = {{Interspeech 2022}},
pages ... | Three research directions that have recently advanced the text-to-speech (TTS) field are end-to-end architecture, prosody control modeling, and on-the-fly duration alignment of non-auto-regressive models. However, these three agendas have yet to be tackled at once in a single solution. Current studies are limited eithe... | null | null |
lim22_interspeech | JETS: Jointly Training FastSpeech2 and HiFi-GAN for End to End Text to Speech | [
"Dan Lim",
"Sunghee Jung",
"Eesung Kim"
] | https://www.isca-archive.org/interspeech_2022/lim22_interspeech.html | https://www.isca-archive.org/interspeech_2022/lim22_interspeech.pdf | 10.21437/Interspeech.2022-10294 | 21-25 | @inproceedings{lim22_interspeech,
title = {{JETS: Jointly Training FastSpeech2 and HiFi-GAN for End to End Text to Speech}},
author = {Dan Lim and Sunghee Jung and Eesung Kim},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {21--25},
doi = {10.21437/Interspeech.2022-10294},
... | In neural text-to-speech (TTS), two-stage system or a cascade of separately learned models have shown synthesis quality close to human speech. For example, FastSpeech2 transforms an input text to a mel-spectrogram and then HiFi-GAN generates a raw waveform from a mel-spectogram where they are called an acoustic feature... | 2203.16852 | title_snapshot |
turrisi22_interspeech | Interpretable dysarthric speaker adaptation based on optimal-transport | [
"Rosanna Turrisi",
"Leonardo Badino"
] | https://www.isca-archive.org/interspeech_2022/turrisi22_interspeech.html | https://www.isca-archive.org/interspeech_2022/turrisi22_interspeech.pdf | 10.21437/Interspeech.2022-36 | 26-30 | @inproceedings{turrisi22_interspeech,
title = {{Interpretable dysarthric speaker adaptation based on optimal-transport}},
author = {Rosanna Turrisi and Leonardo Badino},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {26--30},
doi = {10.21437/Interspeech.2022-36},
issn ... | This work addresses the mismatch problem between the distribution of training data (source) and testing data (target), in the challenging context of dysarthric speech recognition. We focus on Speaker Adaptation (SA) in command speech recognition, where data from multiple sources (i.e., multiple speakers) are available.... | 2203.07143 | title_snapshot |
yue22_interspeech | Dysarthric Speech Recognition From Raw Waveform with Parametric CNNs | [
"Zhengjun Yue",
"Erfan Loweimi",
"Heidi Christensen",
"Jon Barker",
"Zoran Cvetkovic"
] | https://www.isca-archive.org/interspeech_2022/yue22_interspeech.html | https://www.isca-archive.org/interspeech_2022/yue22_interspeech.pdf | 10.21437/Interspeech.2022-163 | 31-35 | @inproceedings{yue22_interspeech,
title = {{Dysarthric Speech Recognition From Raw Waveform with Parametric CNNs}},
author = {Zhengjun Yue and Erfan Loweimi and Heidi Christensen and Jon Barker and Zoran Cvetkovic},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {31--35},
doi ... | Raw waveform acoustic modelling has recently received increasing attention. Compared with the task-blind hand-crafted features which may discard useful information, representations directly learned from the raw waveform are task-specific and potentially include all task-relevant information. In the context of automatic... | null | null |
prananta22_interspeech | The Effectiveness of Time Stretching for Enhancing Dysarthric Speech for Improved Dysarthric Speech Recognition | [
"Luke Prananta",
"Bence Halpern",
"Siyuan Feng",
"Odette Scharenborg"
] | https://www.isca-archive.org/interspeech_2022/prananta22_interspeech.html | https://www.isca-archive.org/interspeech_2022/prananta22_interspeech.pdf | 10.21437/Interspeech.2022-190 | 36-40 | @inproceedings{prananta22_interspeech,
title = {{The Effectiveness of Time Stretching for Enhancing Dysarthric Speech for Improved Dysarthric Speech Recognition}},
author = {Luke Prananta and Bence Halpern and Siyuan Feng and Odette Scharenborg},
year = {2022},
booktitle = {{Interspeech 2022}},
pa... | In this paper, we investigate several existing and a new state-of-the-art generative adversarial network-based (GAN) voice conversion method for enhancing dysarthric speech for improved dysarthric speech recognition. We compare key components of existing methods as part of a rigorous ablation study to find the most eff... | 2201.04908 | title_snapshot |
violeta22_interspeech | Investigating Self-supervised Pretraining Frameworks for Pathological Speech Recognition | [
"Lester Phillip Violeta",
"Wen Chin Huang",
"Tomoki Toda"
] | https://www.isca-archive.org/interspeech_2022/violeta22_interspeech.html | https://www.isca-archive.org/interspeech_2022/violeta22_interspeech.pdf | 10.21437/Interspeech.2022-10043 | 41-45 | @inproceedings{violeta22_interspeech,
title = {{Investigating Self-supervised Pretraining Frameworks for Pathological Speech Recognition}},
author = {Lester Phillip Violeta and Wen Chin Huang and Tomoki Toda},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {41--45},
doi = {10... | We investigate the performance of self-supervised pretraining frameworks on pathological speech datasets used for automatic speech recognition (ASR). Modern end-to-end models require thousands of hours of data to train well, but only a small number of pathological speech datasets are publicly available. A proven soluti... | 2203.15431 | title_snapshot |
bhat22_interspeech | Improved ASR Performance for Dysarthric Speech Using Two-stage DataAugmentation | [
"Chitralekha Bhat",
"Ashish Panda",
"Helmer Strik"
] | https://www.isca-archive.org/interspeech_2022/bhat22_interspeech.html | https://www.isca-archive.org/interspeech_2022/bhat22_interspeech.pdf | 10.21437/Interspeech.2022-10335 | 46-50 | @inproceedings{bhat22_interspeech,
title = {{Improved ASR Performance for Dysarthric Speech Using Two-stage DataAugmentation}},
author = {Chitralekha Bhat and Ashish Panda and Helmer Strik},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {46--50},
doi = {10.21437/Interspeech.... | Machine learning (ML) and Deep Neural Networks (DNN) have greatly aided the problem of Automatic Speech Recognition (ASR). However, accurate ASR for dysarthric speech remains a serious challenge. Dearth of usable data remains a problem in applying ML and DNN techniques for dysarthric speech recognition. In the current ... | null | null |
hernandez22_interspeech | Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognition | [
"Abner Hernandez",
"Paula Andrea Pérez-Toro",
"Elmar Noeth",
"Juan Rafael Orozco-Arroyave",
"Andreas Maier",
"Seung Hee Yang"
] | https://www.isca-archive.org/interspeech_2022/hernandez22_interspeech.html | https://www.isca-archive.org/interspeech_2022/hernandez22_interspeech.pdf | 10.21437/Interspeech.2022-10674 | 51-55 | @inproceedings{hernandez22_interspeech,
title = {{Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognition}},
author = {Abner Hernandez and Paula Andrea Pérez-Toro and Elmar Noeth and Juan Rafael Orozco-Arroyave and Andreas Maier and Seung Hee Yang},
year = {202... | State-of-the-art automatic speech recognition (ASR) systems perform well on healthy speech. However, the performance on impaired speech still remains an issue. The current study explores the usefulness of using Wav2Vec self-supervised speech representations as features for training an ASR system for dysarthric speech. ... | 2204.01670 | title_snapshot |
lee22b_interspeech | Regularizing Transformer-based Acoustic Models by Penalizing Attention Weights | [
"Munhak Lee",
"Joon-Hyuk Chang",
"Sang-Eon Lee",
"Ju-Seok Seong",
"Chanhee Park",
"Haeyoung Kwon"
] | https://www.isca-archive.org/interspeech_2022/lee22b_interspeech.html | https://www.isca-archive.org/interspeech_2022/lee22b_interspeech.pdf | 10.21437/Interspeech.2022-362 | 56-60 | @inproceedings{lee22b_interspeech,
title = {{Regularizing Transformer-based Acoustic Models by Penalizing Attention Weights}},
author = {Munhak Lee and Joon-Hyuk Chang and Sang-Eon Lee and Ju-Seok Seong and Chanhee Park and Haeyoung Kwon},
year = {2022},
booktitle = {{Interspeech 2022}},
pages ... | The application of deep learning has significantly advanced the performance of automatic speech recognition (ASR) systems. Various components make up an ASR system, such as the acoustic model (AM), language model, and lexicon. Generally, the AM has benefited the most from deep learning. Numerous types of neural network... | null | null |
chan22_interspeech | Content-Context Factorized Representations for Automated Speech Recognition | [
"David Chan",
"Shalini Ghosh"
] | https://www.isca-archive.org/interspeech_2022/chan22_interspeech.html | https://www.isca-archive.org/interspeech_2022/chan22_interspeech.pdf | 10.21437/Interspeech.2022-390 | 61-65 | @inproceedings{chan22_interspeech,
title = {{Content-Context Factorized Representations for Automated Speech Recognition}},
author = {David Chan and Shalini Ghosh},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {61--65},
doi = {10.21437/Interspeech.2022-390},
issn = {... | Deep neural networks have largely demonstrated their ability to perform automated speech recognition (ASR) by extracting meaningful features from input audio frames. Such features, however, may consist not only of information about the spoken language content, but also may contain information about unnecessary contexts... | 2205.09872 | title_snapshot |
karakasidis22_interspeech | Comparison and Analysis of New Curriculum Criteria for End-to-End ASR | [
"Georgios Karakasidis",
"Tamás Grósz",
"Mikko Kurimo"
] | https://www.isca-archive.org/interspeech_2022/karakasidis22_interspeech.html | https://www.isca-archive.org/interspeech_2022/karakasidis22_interspeech.pdf | 10.21437/Interspeech.2022-10046 | 66-70 | @inproceedings{karakasidis22_interspeech,
title = {{Comparison and Analysis of New Curriculum Criteria for End-to-End ASR}},
author = {Georgios Karakasidis and Tamás Grósz and Mikko Kurimo},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {66--70},
doi = {10.21437/Interspeec... | It is common knowledge that the quantity and quality of the training data play a significant role in the creation of a good machine learning model. In this paper, we take it one step further and demonstrate that the way the training examples are arranged is also of crucial importance. Curriculum Learning is built on th... | 2208.05782 | title_snapshot |
baby22_interspeech | Incremental learning for RNN-Transducer based speech recognition models | [
"Deepak Baby",
"Pasquale D'Alterio",
"Valentin Mendelev"
] | https://www.isca-archive.org/interspeech_2022/baby22_interspeech.html | https://www.isca-archive.org/interspeech_2022/baby22_interspeech.pdf | 10.21437/Interspeech.2022-10795 | 71-75 | @inproceedings{baby22_interspeech,
title = {{Incremental learning for RNN-Transducer based speech recognition models}},
author = {Deepak Baby and Pasquale D'Alterio and Valentin Mendelev},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {71--75},
doi = {10.21437/Interspeech.20... | This paper investigates an incremental learning framework for a real-world voice assistant employing RNN-Transducer based automatic speech recognition (ASR) model. Such a model needs to be regularly updated to keep up with changing distribution of customer requests. We demonstrate that a simple fine-tuning approach wit... | null | null |
hard22_interspeech | Production federated keyword spotting via distillation, filtering, and joint federated-centralized training | [
"Andrew Hard",
"Kurt Partridge",
"Neng Chen",
"Sean Augenstein",
"Aishanee Shah",
"Hyun Jin Park",
"Alex Park",
"Sara Ng",
"Jessica Nguyen",
"Ignacio Lopez-Moreno",
"Rajiv Mathews",
"Francoise Beaufays"
] | https://www.isca-archive.org/interspeech_2022/hard22_interspeech.html | https://www.isca-archive.org/interspeech_2022/hard22_interspeech.pdf | 10.21437/Interspeech.2022-11050 | 76-80 | @inproceedings{hard22_interspeech,
title = {{Production federated keyword spotting via distillation, filtering, and joint federated-centralized training}},
author = {Andrew Hard and Kurt Partridge and Neng Chen and Sean Augenstein and Aishanee Shah and Hyun Jin Park and Alex Park and Sara Ng and Jessica Nguy... | We trained a keyword spotting model using federated learning on real user devices and observed significant improvements when the model was deployed for inference on phones. To compensate for data domains that are missing from on-device training caches, we employed joint federated-centralized training. And to learn in t... | 2204.06322 | title_snapshot |
song22b_interspeech | Use of prosodic and lexical cues for disambiguating wh-words in Korean | [
"Jieun Song",
"Hae-Sung Jeon",
"Jieun Kiaer"
] | https://www.isca-archive.org/interspeech_2022/song22b_interspeech.html | https://www.isca-archive.org/interspeech_2022/song22b_interspeech.pdf | 10.21437/Interspeech.2022-561 | 81-85 | @inproceedings{song22b_interspeech,
title = {{Use of prosodic and lexical cues for disambiguating wh-words in Korean}},
author = {Jieun Song and Hae-Sung Jeon and Jieun Kiaer},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {81--85},
doi = {10.21437/Interspeech.2022-561},
i... | Previous research has shown that the ambiguity of wh-words in Korean can be resolved by prosody. The present study investigated the interplay between prosody and lexical cues in disambiguation. Our written survey results showed that the use of certain adverbs (e.g., a little, once) with a wh-word increases the likeliho... | null | null |
ribeiro22_interspeech | Autoencoder-Based Tongue Shape Estimation During Continuous Speech | [
"Vinicius Ribeiro",
"Yves Laprie"
] | https://www.isca-archive.org/interspeech_2022/ribeiro22_interspeech.html | https://www.isca-archive.org/interspeech_2022/ribeiro22_interspeech.pdf | 10.21437/Interspeech.2022-10272 | 86-90 | @inproceedings{ribeiro22_interspeech,
title = {{Autoencoder-Based Tongue Shape Estimation During Continuous Speech}},
author = {Vinicius Ribeiro and Yves Laprie},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {86--90},
doi = {10.21437/Interspeech.2022-10272},
issn = {... | Vocal tract shape estimation is a necessary step for articulatory speech synthesis. However, the literature on the topic is scarce, and most current methods lack adequacy to many physical constraints related to speech production. This study proposes an alternative approach to the task to solve specific issues faced in ... | null | null |
magistro22_interspeech | Phonetic erosion and information structure in function words: the case of mia | [
"Giuseppe Magistro",
"Claudia Crocco"
] | https://www.isca-archive.org/interspeech_2022/magistro22_interspeech.html | https://www.isca-archive.org/interspeech_2022/magistro22_interspeech.pdf | 10.21437/Interspeech.2022-10305 | 91-95 | @inproceedings{magistro22_interspeech,
title = {{Phonetic erosion and information structure in function words: the case of mia}},
author = {Giuseppe Magistro and Claudia Crocco},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {91--95},
doi = {10.21437/Interspeech.2022-10305},... | The purpose of this paper is to examine the prosodic correlates of a grammaticalisation process that leads to the formation of a function word. In particular, our case study will tackle the pattern of negation renewal known as Jespersen's Cycle (JC). In JC, a negative reinforcer carrying contrastive meaning grammatical... | null | null |
oh22_interspeech | Dynamic Vertical Larynx Actions Under Prosodic Focus | [
"Miran Oh",
"Yoonjeong Lee"
] | https://www.isca-archive.org/interspeech_2022/oh22_interspeech.html | https://www.isca-archive.org/interspeech_2022/oh22_interspeech.pdf | 10.21437/Interspeech.2022-10661 | 96-100 | @inproceedings{oh22_interspeech,
title = {{Dynamic Vertical Larynx Actions Under Prosodic Focus}},
author = {Miran Oh and Yoonjeong Lee},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {96--100},
doi = {10.21437/Interspeech.2022-10661},
issn = {2958-1796},
} | Recently, Lee (2018) observes that one vertical larynx movement (VLM) is associated with an Accentual Phrase (AP) in Seoul Korean. The current study builds on these findings by investigating the effect of prosodic focus on vertical larynx actions. Target sentences were designed to produce four APs (e.g., Joohyun sold s... | null | null |
bradshaw22_interspeech | Fundamental Frequency Variability over Time in Telephone Interactions | [
"Leah Bradshaw",
"Eleanor Chodroff",
"Lena Jäger",
"Volker Dellwo"
] | https://www.isca-archive.org/interspeech_2022/bradshaw22_interspeech.html | https://www.isca-archive.org/interspeech_2022/bradshaw22_interspeech.pdf | 10.21437/Interspeech.2022-10669 | 101-105 | @inproceedings{bradshaw22_interspeech,
title = {{Fundamental Frequency Variability over Time in Telephone Interactions}},
author = {Leah Bradshaw and Eleanor Chodroff and Lena Jäger and Volker Dellwo},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {101--105},
doi = {10.2143... | Speech signals contain substantial fundamental frequency (f0) variability. Even within a single utterance, speakers modify f0 to create different intonational patterns. Previous studies have identified markers of increased f0 variability, such as the introduction of a new topic or greetings, but these are limited in th... | null | null |
tsiamas22_interspeech | SHAS: Approaching optimal Segmentation for End-to-End Speech Translation | [
"Ioannis Tsiamas",
"Gerard I. Gállego",
"José A. R. Fonollosa",
"Marta R. Costa-jussÃ"
] | https://www.isca-archive.org/interspeech_2022/tsiamas22_interspeech.html | https://www.isca-archive.org/interspeech_2022/tsiamas22_interspeech.pdf | 10.21437/Interspeech.2022-59 | 106-110 | @inproceedings{tsiamas22_interspeech,
title = {{SHAS: Approaching optimal Segmentation for End-to-End Speech Translation}},
author = {Ioannis Tsiamas and Gerard I. Gállego and José A. R. Fonollosa and Marta R. Costa-jussà },
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {106--110}... | Speech translation models are unable to directly process long audios, like TED talks, which have to be split into shorter segments. Speech translation datasets provide manual segmentations of the audios, which are not available in real-world scenarios, and existing segmentation methods usually significantly reduce tran... | 2202.04774 | title_snapshot |
zhao22g_interspeech | M-Adapter: Modality Adaptation for End-to-End Speech-to-Text Translation | [
"Jinming Zhao",
"Hao Yang",
"Gholamreza Haffari",
"Ehsan Shareghi"
] | https://www.isca-archive.org/interspeech_2022/zhao22g_interspeech.html | https://www.isca-archive.org/interspeech_2022/zhao22g_interspeech.pdf | 10.21437/Interspeech.2022-592 | 111-115 | @inproceedings{zhao22g_interspeech,
title = {{M-Adapter: Modality Adaptation for End-to-End Speech-to-Text Translation}},
author = {Jinming Zhao and Hao Yang and Gholamreza Haffari and Ehsan Shareghi},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {111--115},
doi = {10.21437... | End-to-end speech-to-text translation models are often initialized with pre-trained speech encoder and pre-trained text decoder. This leads to a significant training gap between pre-training and fine-tuning, largely due to the modality differences between speech outputs from the encoder and text inputs to the decoder. ... | 2207.00952 | title_snapshot |
zaidi22_interspeech | Cross-Modal Decision Regularization for Simultaneous Speech Translation | [
"Mohd Abbas Zaidi",
"Beomseok Lee",
"Sangha Kim",
"Chanwoo Kim"
] | https://www.isca-archive.org/interspeech_2022/zaidi22_interspeech.html | https://www.isca-archive.org/interspeech_2022/zaidi22_interspeech.pdf | 10.21437/Interspeech.2022-10617 | 116-120 | @inproceedings{zaidi22_interspeech,
title = {{Cross-Modal Decision Regularization for Simultaneous Speech Translation}},
author = {Mohd Abbas Zaidi and Beomseok Lee and Sangha Kim and Chanwoo Kim},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {116--120},
doi = {10.21437/Int... | Simultaneous translation systems start producing the output while processing the partial source sentence in the incoming input stream. These systems need to decide when to read more input and when to write the output. The decisions taken by the model depend on the structure of source/target language and the information... | 2110.15729 | title_judge |
fukuda22b_interspeech | Speech Segmentation Optimization using Segmented Bilingual Speech Corpus for End-to-end Speech Translation | [
"Ryo Fukuda",
"Katsuhito Sudoh",
"Satoshi Nakamura"
] | https://www.isca-archive.org/interspeech_2022/fukuda22b_interspeech.html | https://www.isca-archive.org/interspeech_2022/fukuda22b_interspeech.pdf | 10.21437/Interspeech.2022-11382 | 121-125 | @inproceedings{fukuda22b_interspeech,
title = {{Speech Segmentation Optimization using Segmented Bilingual Speech Corpus for End-to-end Speech Translation}},
author = {Ryo Fukuda and Katsuhito Sudoh and Satoshi Nakamura},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {121--125},
d... | Speech segmentation, which splits long speech into short segments, is essential for speech translation (ST). Popular VAD tools like WebRTC VAD have generally relied on pause-based segmentation. Unfortunately, pauses in speech do not necessarily match sentence boundaries, and sentences can be connected by a very short p... | 2203.15479 | title_snapshot |
r22_interspeech | Generalized Keyword Spotting using ASR embeddings | [
"Kirandevraj R",
"Vinod Kumar Kurmi",
"Vinay Namboodiri",
"C V Jawahar"
] | https://www.isca-archive.org/interspeech_2022/r22_interspeech.html | https://www.isca-archive.org/interspeech_2022/r22_interspeech.pdf | 10.21437/Interspeech.2022-10450 | 126-130 | @inproceedings{r22_interspeech,
title = {{Generalized Keyword Spotting using ASR embeddings}},
author = {Kirandevraj R and Vinod Kumar Kurmi and Vinay Namboodiri and C V Jawahar},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {126--130},
doi = {10.21437/Interspeech.2022-1045... | Keyword Spotting (KWS) detects a set of pre-defined spoken keywords. Building a KWS system for an arbitrary set requires massive training datasets. We propose to use the text transcripts from an Automatic Speech Recognition (ASR) system alongside triplets for KWS training. The intermediate representation from the ASR s... | null | null |
ahn22_interspeech | Multi-Corpus Speech Emotion Recognition for Unseen Corpus Using Corpus-Wise Weights in Classification Loss | [
"Youngdo Ahn",
"Sung Joo Lee",
"Jong Won Shin"
] | https://www.isca-archive.org/interspeech_2022/ahn22_interspeech.html | https://www.isca-archive.org/interspeech_2022/ahn22_interspeech.pdf | 10.21437/Interspeech.2022-111 | 131-135 | @inproceedings{ahn22_interspeech,
title = {{Multi-Corpus Speech Emotion Recognition for Unseen Corpus Using Corpus-Wise Weights in Classification Loss}},
author = {Youngdo Ahn and Sung Joo Lee and Jong Won Shin},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {131--135},
doi ... | Since each of the currently available emotional speech corpora is rather small to deal with personal or cultural diversity, multiple emotional speech corpora can be jointly used to train a speech emotion recognition (SER) model robust to unseen corpora. Each corpus has different characteristics, including whether acted... | null | null |
kim22d_interspeech | Improving Speech Emotion Recognition Through Focus and Calibration Attention Mechanisms | [
"Junghun Kim",
"Yoojin An",
"Jihie Kim"
] | https://www.isca-archive.org/interspeech_2022/kim22d_interspeech.html | https://www.isca-archive.org/interspeech_2022/kim22d_interspeech.pdf | 10.21437/Interspeech.2022-299 | 136-140 | @inproceedings{kim22d_interspeech,
title = {{Improving Speech Emotion Recognition Through Focus and Calibration Attention Mechanisms}},
author = {Junghun Kim and Yoojin An and Jihie Kim},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {136--140},
doi = {10.21437/Interspeech.2... | Attention has become one of the most commonly used mechanisms in deep learning approaches. The attention mechanism can help the system focus more on the feature space's critical regions. For example, high amplitude regions can play an important role for Speech Emotion Recognition (SER). In this paper, we identify misal... | 2208.10491 | title_snapshot |
lee22e_interspeech | The Emotion is Not One-hot Encoding: Learning with Grayscale Label for Emotion Recognition in Conversation | [
"Joosung Lee"
] | https://www.isca-archive.org/interspeech_2022/lee22e_interspeech.html | https://www.isca-archive.org/interspeech_2022/lee22e_interspeech.pdf | 10.21437/Interspeech.2022-551 | 141-145 | @inproceedings{lee22e_interspeech,
title = {{The Emotion is Not One-hot Encoding: Learning with Grayscale Label for Emotion Recognition in Conversation}},
author = {Joosung Lee},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {141--145},
doi = {10.21437/Interspeech.2022-551},... | In emotion recognition in conversation (ERC), the emotion of the current utterance is predicted by considering the previous context, which can be utilized in many natural language processing tasks. Although multiple emotions can coexist in a given sentence, most previous approaches take the perspective of a classificat... | 2206.07359 | title_snapshot |
triantafyllopoulos22b_interspeech | Probing speech emotion recognition transformers for linguistic knowledge | [
"Andreas Triantafyllopoulos",
"Johannes Wagner",
"Hagen Wierstorf",
"Maximilian Schmitt",
"Uwe Reichel",
"Florian Eyben",
"Felix Burkhardt",
"Björn W. Schuller"
] | https://www.isca-archive.org/interspeech_2022/triantafyllopoulos22b_interspeech.html | https://www.isca-archive.org/interspeech_2022/triantafyllopoulos22b_interspeech.pdf | 10.21437/Interspeech.2022-10371 | 146-150 | @inproceedings{triantafyllopoulos22b_interspeech,
title = {{Probing speech emotion recognition transformers for linguistic knowledge}},
author = {Andreas Triantafyllopoulos and Johannes Wagner and Hagen Wierstorf and Maximilian Schmitt and Uwe Reichel and Florian Eyben and Felix Burkhardt and Björn W. Schul... | Large, pre-trained neural networks consisting of self-attention layers (transformers) have recently achieved state-of-the-art results on several speech emotion recognition (SER) datasets. These models are typically pre-trained in self-supervised manner with the goal to improve automatic speech recognition performance -... | 2204.00400 | title_snapshot |
prabhu22_interspeech | End-To-End Label Uncertainty Modeling for Speech-based Arousal Recognition Using Bayesian Neural Networks | [
"Navin Raj Prabhu",
"Guillaume Carbajal",
"Nale Lehmann-Willenbrock",
"Timo Gerkmann"
] | https://www.isca-archive.org/interspeech_2022/prabhu22_interspeech.html | https://www.isca-archive.org/interspeech_2022/prabhu22_interspeech.pdf | 10.21437/Interspeech.2022-10490 | 151-155 | @inproceedings{prabhu22_interspeech,
title = {{End-To-End Label Uncertainty Modeling for Speech-based Arousal Recognition Using Bayesian Neural Networks}},
author = {Navin Raj Prabhu and Guillaume Carbajal and Nale Lehmann-Willenbrock and Timo Gerkmann},
year = {2022},
booktitle = {{Interspeech 2022... | Emotions are subjective constructs. Recent end-to-end speech emotion recognition systems are typically agnostic to the subjective nature of emotions, despite their state-of-the-art performance. In this work, we introduce an end-to-end Bayesian neural network architecture to capture the inherent subjectivity in the arou... | 2110.03299 | title_snapshot |
perez22_interspeech | Mind the gap: On the value of silence representations to lexical-based speech emotion recognition | [
"Matthew Perez",
"Mimansa Jaiswal",
"Minxue Niu",
"Cristina Gorrostieta",
"Matthew Roddy",
"Kye Taylor",
"Reza Lotfian",
"John Kane",
"Emily Mower Provost"
] | https://www.isca-archive.org/interspeech_2022/perez22_interspeech.html | https://www.isca-archive.org/interspeech_2022/perez22_interspeech.pdf | 10.21437/Interspeech.2022-10943 | 156-160 | @inproceedings{perez22_interspeech,
title = {{Mind the gap: On the value of silence representations to lexical-based speech emotion recognition}},
author = {Matthew Perez and Mimansa Jaiswal and Minxue Niu and Cristina Gorrostieta and Matthew Roddy and Kye Taylor and Reza Lotfian and John Kane and Emily Mowe... | Speech timing and non-speech regions (here referred to as ``silence"), often play a critical role in the perception of spoken language. Silence represents an important paralinguistic component in communication. For example, some of its functions include conveying emphasis, dramatization, or even sarcasm. In speech emot... | null | null |
chou22_interspeech | Exploiting Co-occurrence Frequency of Emotions in Perceptual Evaluations To Train A Speech Emotion Classifier | [
"Huang-Cheng Chou",
"Chi-Chun Lee",
"Carlos Busso"
] | https://www.isca-archive.org/interspeech_2022/chou22_interspeech.html | https://www.isca-archive.org/interspeech_2022/chou22_interspeech.pdf | 10.21437/Interspeech.2022-11041 | 161-165 | @inproceedings{chou22_interspeech,
title = {{Exploiting Co-occurrence Frequency of Emotions in Perceptual Evaluations To Train A Speech Emotion Classifier}},
author = {Huang-Cheng Chou and Chi-Chun Lee and Carlos Busso},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {161--165},
do... | Previous studies on speech emotion recognition (SER) with categorical emotions have often formulated the task as a single-label classification problem, where the emotions are considered orthogonal to each other. However, previous studies have indicated that emotions can co-occur, especially for more ambiguous emotional... | null | null |
dhamyal22_interspeech | Positional Encoding for Capturing Modality Specific Cadence for Emotion Detection | [
"Hira Dhamyal",
"Bhiksha Raj",
"Rita Singh"
] | https://www.isca-archive.org/interspeech_2022/dhamyal22_interspeech.html | https://www.isca-archive.org/interspeech_2022/dhamyal22_interspeech.pdf | 10.21437/Interspeech.2022-11085 | 166-170 | @inproceedings{dhamyal22_interspeech,
title = {{Positional Encoding for Capturing Modality Specific Cadence for Emotion Detection}},
author = {Hira Dhamyal and Bhiksha Raj and Rita Singh},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {166--170},
doi = {10.21437/Interspeech.... | Emotion detection from a single modality, such as an audio or text stream, has been known to be a challenging task. While encouraging results have been obtained by using joint evidence from multiple streams, combining such evidence in optimal ways is an open challenge. In this paper, we claim that although the multi-mo... | null | null |
vuho22_interspeech | Speak Like a Professional: Increasing Speech Intelligibility by Mimicking Professional Announcer Voice with Voice Conversion | [
"Tuan Vu Ho",
"Maori Kobayashi",
"Masato Akagi"
] | https://www.isca-archive.org/interspeech_2022/vuho22_interspeech.html | https://www.isca-archive.org/interspeech_2022/vuho22_interspeech.pdf | 10.21437/Interspeech.2022-124 | 171-175 | @inproceedings{vuho22_interspeech,
title = {{Speak Like a Professional: Increasing Speech Intelligibility by Mimicking Professional Announcer Voice with Voice Conversion}},
author = {Tuan {Vu Ho} and Maori Kobayashi and Masato Akagi},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {1... | In most of practical scenarios, the announcement system must deliver speech messages in a noisy environment, in which the background noise cannot be cancelled out. The local noise reduces speech intelligibility and increases listening effort of the listener, hence hamper the effectiveness of announcement system. There ... | 2206.13021 | title_snapshot |
ho22_interspeech | Vector-quantized Variational Autoencoder for Phase-aware Speech Enhancement | [
"Tuan Vu Ho",
"Quoc Huy Nguyen",
"Masato Akagi",
"Masashi Unoki"
] | https://www.isca-archive.org/interspeech_2022/ho22_interspeech.html | https://www.isca-archive.org/interspeech_2022/ho22_interspeech.pdf | 10.21437/Interspeech.2022-443 | 176-180 | @inproceedings{ho22_interspeech,
title = {{Vector-quantized Variational Autoencoder for Phase-aware Speech Enhancement}},
author = {Tuan Vu Ho and Quoc Huy Nguyen and Masato Akagi and Masashi Unoki},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {176--180},
doi = {10.21437/I... | Recent speech enhancement methods based on the complex ideal ratio mask (cIRM) have achieved promising results. These methods often deploy a deep neural network to jointly estimate the real and imaginary components of the cIRM defined in the complex domain. However, the unbounded property of cIRM poses difficulties whe... | null | null |
kim22i_interspeech | iDeepMMSE: An improved deep learning approach to MMSE speech and noise power spectrum estimation for speech enhancement | [
"Minseung Kim",
"Hyungchan Song",
"Sein Cheong",
"Jong Won Shin"
] | https://www.isca-archive.org/interspeech_2022/kim22i_interspeech.html | https://www.isca-archive.org/interspeech_2022/kim22i_interspeech.pdf | 10.21437/Interspeech.2022-964 | 181-185 | @inproceedings{kim22i_interspeech,
title = {{iDeepMMSE: An improved deep learning approach to MMSE speech and noise power spectrum estimation for speech enhancement}},
author = {Minseung Kim and Hyungchan Song and Sein Cheong and Jong Won Shin},
year = {2022},
booktitle = {{Interspeech 2022}},
pag... | Deep learning approaches have been successfully applied to single channel speech enhancement exhibiting significant performance improvement. Recently, approaches unifying deep learning techniques into a statistical speech enhancement framework were proposed, including Deep Xi and DeepMMSE in which a priori signal-to-no... | null | null |
hung22_interspeech | Boosting Self-Supervised Embeddings for Speech Enhancement | [
"Kuo-Hsuan Hung",
"Szu-wei Fu",
"Huan-Hsin Tseng",
"Hsin-Tien Chiang",
"Yu Tsao",
"Chii-Wann Lin"
] | https://www.isca-archive.org/interspeech_2022/hung22_interspeech.html | https://www.isca-archive.org/interspeech_2022/hung22_interspeech.pdf | 10.21437/Interspeech.2022-10002 | 186-190 | @inproceedings{hung22_interspeech,
title = {{Boosting Self-Supervised Embeddings for Speech Enhancement}},
author = {Kuo-Hsuan Hung and Szu-wei Fu and Huan-Hsin Tseng and Hsin-Tien Chiang and Yu Tsao and Chii-Wann Lin},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {186--190},
doi... | Self-supervised learning (SSL) representation for speech has achieved state-of-the-art (SOTA) performance on several downstream tasks. However, there remains room for improvement in speech enhancement (SE) tasks. In this study, we used a cross-domain feature to solve the problem that SSL embeddings may lack fine-graine... | 2204.03339 | title_snapshot |
hwang22b_interspeech | Monoaural Speech Enhancement Using a Nested U-Net with Two-Level Skip Connections | [
"Seorim Hwang",
"Sung Wook Park",
"Youngcheol Park"
] | https://www.isca-archive.org/interspeech_2022/hwang22b_interspeech.html | https://www.isca-archive.org/interspeech_2022/hwang22b_interspeech.pdf | 10.21437/Interspeech.2022-10025 | 191-195 | @inproceedings{hwang22b_interspeech,
title = {{Monoaural Speech Enhancement Using a Nested U-Net with Two-Level Skip Connections}},
author = {Seorim Hwang and Sung Wook Park and Youngcheol Park},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {191--195},
doi = {10.21437/Inter... | Capturing the contextual information in multi-scale is known to be beneficial for improving the performance of DNN-based speech enhancement (SE) models. This paper proposes a new SE model, called NUNet-TLS, having two-level skip connections between the residual U-Blocks nested in each layer of a large U-Net structure. ... | null | null |
muckenhirn22_interspeech | CycleGAN-based Unpaired Speech Dereverberation | [
"Hannah Muckenhirn",
"Aleksandr Safin",
"Hakan Erdogan",
"Felix de Chaumont Quitry",
"Marco Tagliasacchi",
"Scott Wisdom",
"John R. Hershey"
] | https://www.isca-archive.org/interspeech_2022/muckenhirn22_interspeech.html | https://www.isca-archive.org/interspeech_2022/muckenhirn22_interspeech.pdf | 10.21437/Interspeech.2022-10104 | 196-200 | @inproceedings{muckenhirn22_interspeech,
title = {{CycleGAN-based Unpaired Speech Dereverberation}},
author = {Hannah Muckenhirn and Aleksandr Safin and Hakan Erdogan and Felix {de Chaumont Quitry} and Marco Tagliasacchi and Scott Wisdom and John R. Hershey},
year = {2022},
booktitle = {{Interspeech... | Typically, neural network-based speech dereverberation models are trained on paired data, composed of a dry utterance and its corresponding reverberant utterance. The main limitation of this approach is that such models can only be trained on large amounts of data and a variety of room impulse responses when the data i... | 2203.15652 | title_snapshot |
pandey22_interspeech | Attentive Training: A New Training Framework for Talker-independent Speaker Extraction | [
"Ashutosh Pandey",
"DeLiang Wang"
] | https://www.isca-archive.org/interspeech_2022/pandey22_interspeech.html | https://www.isca-archive.org/interspeech_2022/pandey22_interspeech.pdf | 10.21437/Interspeech.2022-10491 | 201-205 | @inproceedings{pandey22_interspeech,
title = {{Attentive Training: A New Training Framework for Talker-independent Speaker Extraction}},
author = {Ashutosh Pandey and DeLiang Wang},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {201--205},
doi = {10.21437/Interspeech.2022-10... | Listening in a multitalker scenario, we typically attend to a single talker through auditory selective attention. Inspired by human selective attention, we propose attentive training: a new training framework for talker-independent speaker extraction with an intrinsic selection mechanism. In the real world, multiple ta... | null | null |
vuong22_interspeech | Improved Modulation-Domain Loss for Neural-Network-based Speech Enhancement | [
"Tyler Vuong",
"Richard Stern"
] | https://www.isca-archive.org/interspeech_2022/vuong22_interspeech.html | https://www.isca-archive.org/interspeech_2022/vuong22_interspeech.pdf | 10.21437/Interspeech.2022-11082 | 206-210 | @inproceedings{vuong22_interspeech,
title = {{Improved Modulation-Domain Loss for Neural-Network-based Speech Enhancement}},
author = {Tyler Vuong and Richard Stern},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {206--210},
doi = {10.21437/Interspeech.2022-11082},
issn ... | We describe an improved modulation-domain loss for deeplearning- based speech enhancement systems (SE). We utilized a simple self-supervised speech reconstruction task to learn a set of spectro-temporal receptive fields (STRFs). Similar to the recently developed spectro-temporal modulation error, the learned STRFs are ... | null | null |
peng22d_interspeech | Perceptual Characteristics Based Multi-objective Model for Speech Enhancement | [
"Chiang-Jen Peng",
"Yun-Ju Chan",
"Yih-Liang Shen",
"Cheng Yu",
"Yu Tsao",
"Tai-Shih Chi"
] | https://www.isca-archive.org/interspeech_2022/peng22d_interspeech.html | https://www.isca-archive.org/interspeech_2022/peng22d_interspeech.pdf | 10.21437/Interspeech.2022-11197 | 211-215 | @inproceedings{peng22d_interspeech,
title = {{Perceptual Characteristics Based Multi-objective Model for Speech Enhancement}},
author = {Chiang-Jen Peng and Yun-Ju Chan and Yih-Liang Shen and Cheng Yu and Yu Tsao and Tai-Shih Chi},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {211-... | Deep learning has been widely adopted for speech applications. Many studies have shown that using the multiple objective framework and learned deep features is effective for improving system performance. In this paper, we propose a perceptual characteristics based multi-objective speech enhancement (SE) algorithm that ... | null | null |
delcroix22_interspeech | Listen only to me! How well can target speech extraction handle false alarms? | [
"Marc Delcroix",
"Keisuke Kinoshita",
"Tsubasa Ochiai",
"Katerina Zmolikova",
"Hiroshi Sato",
"Tomohiro Nakatani"
] | https://www.isca-archive.org/interspeech_2022/delcroix22_interspeech.html | https://www.isca-archive.org/interspeech_2022/delcroix22_interspeech.pdf | 10.21437/Interspeech.2022-11252 | 216-220 | @inproceedings{delcroix22_interspeech,
title = {{Listen only to me! How well can target speech extraction handle false alarms?}},
author = {Marc Delcroix and Keisuke Kinoshita and Tsubasa Ochiai and Katerina Zmolikova and Hiroshi Sato and Tomohiro Nakatani},
year = {2022},
booktitle = {{Interspeech ... | Target speech extraction (TSE) extracts the speech of a target speaker in a mixture given auxiliary clues characterizing the speaker, such as an enrollment utterance. TSE addresses thus the challenging problem of simultaneously performing separation and speaker identification. There has been much progress in extraction... | 2204.04811 | title_snapshot |
shi22e_interspeech | Monaural Speech Enhancement Based on Spectrogram Decomposition for Convolutional Neural Network-sensitive Feature Extraction | [
"Hao Shi",
"Longbiao Wang",
"Sheng Li",
"Jianwu Dang",
"Tatsuya Kawahara"
] | https://www.isca-archive.org/interspeech_2022/shi22e_interspeech.html | https://www.isca-archive.org/interspeech_2022/shi22e_interspeech.pdf | 10.21437/Interspeech.2022-11268 | 221-225 | @inproceedings{shi22e_interspeech,
title = {{Monaural Speech Enhancement Based on Spectrogram Decomposition for Convolutional Neural Network-sensitive Feature Extraction}},
author = {Hao Shi and Longbiao Wang and Sheng Li and Jianwu Dang and Tatsuya Kawahara},
year = {2022},
booktitle = {{Interspeec... | Many state-of-the-art speech enhancement (SE) systems have recently used convolutional neural networks (CNNs) to extract multi-scale feature maps. However, CNN relies more on local texture than global shape, which is more susceptible to degraded spectrogram and may fail to capture the detailed structure of speech. Alth... | null | null |
lemercier22_interspeech | Neural Network-augmented Kalman Filtering for Robust Online Speech Dereverberation in Noisy Reverberant Environments | [
"Jean-Marie Lemercier",
"Joachim Thiemann",
"Raphael Koning",
"Timo Gerkmann"
] | https://www.isca-archive.org/interspeech_2022/lemercier22_interspeech.html | https://www.isca-archive.org/interspeech_2022/lemercier22_interspeech.pdf | 10.21437/Interspeech.2022-11337 | 226-230 | @inproceedings{lemercier22_interspeech,
title = {{Neural Network-augmented Kalman Filtering for Robust Online Speech Dereverberation in Noisy Reverberant Environments}},
author = {Jean-Marie Lemercier and Joachim Thiemann and Raphael Koning and Timo Gerkmann},
year = {2022},
booktitle = {{Interspeec... | In this paper, a neural network-augmented algorithm for noise-robust online dereverberation with a Kalman filtering variant of the weighted prediction error (WPE) method is proposed. The filter stochastic variations are predicted by a deep neural network (DNN) trained end-to-end using the filter residual error and sign... | 2204.02741 | title_snapshot |
schmidt22_interspeech | PodcastMix: A dataset for separating music and speech in podcasts | [
"Nicolás Schmidt",
"Jordi Pons",
"Marius Miron"
] | https://www.isca-archive.org/interspeech_2022/schmidt22_interspeech.html | https://www.isca-archive.org/interspeech_2022/schmidt22_interspeech.pdf | 10.21437/Interspeech.2022-41 | 231-235 | @inproceedings{schmidt22_interspeech,
title = {{PodcastMix: A dataset for separating music and speech in podcasts}},
author = {Nicolás Schmidt and Jordi Pons and Marius Miron},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {231--235},
doi = {10.21437/Interspeech.2022-41},
... | We introduce PodcastMix, a dataset formalizing the task of separating background music and foreground speech in podcasts. We aim at defining a benchmark suitable for training and evaluating (deep learning) source separation models. To that end, we release a large and diverse training dataset based on programatically ge... | 2207.07403 | title_snapshot |
saijo22_interspeech | Independence-based Joint Dereverberation and Separation with Neural Source Model | [
"Kohei Saijo",
"Robin Scheibler"
] | https://www.isca-archive.org/interspeech_2022/saijo22_interspeech.html | https://www.isca-archive.org/interspeech_2022/saijo22_interspeech.pdf | 10.21437/Interspeech.2022-271 | 236-240 | @inproceedings{saijo22_interspeech,
title = {{Independence-based Joint Dereverberation and Separation with Neural Source Model}},
author = {Kohei Saijo and Robin Scheibler},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {236--240},
doi = {10.21437/Interspeech.2022-271},
is... | We propose an independence-based joint dereverberation and separation method with a neural source model. We introduce a neural network in the framework of time-decorrelation iterative source steering, which is an extension of independent vector analysis to joint dereverberation and separation. The network is trained in... | 2110.06545 | title_snapshot |
saijo22b_interspeech | Spatial Loss for Unsupervised Multi-channel Source Separation | [
"Kohei Saijo",
"Robin Scheibler"
] | https://www.isca-archive.org/interspeech_2022/saijo22b_interspeech.html | https://www.isca-archive.org/interspeech_2022/saijo22b_interspeech.pdf | 10.21437/Interspeech.2022-274 | 241-245 | @inproceedings{saijo22b_interspeech,
title = {{Spatial Loss for Unsupervised Multi-channel Source Separation}},
author = {Kohei Saijo and Robin Scheibler},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {241--245},
doi = {10.21437/Interspeech.2022-274},
issn = {2958-17... | We propose a spatial loss for unsupervised multi-channel source separation. The proposed loss exploits the duality of direction of arrival (DOA) and beamforming: the steering and beamforming vectors should be aligned for the target source, but orthogonal for interfering ones. The spatial loss encourages consistency bet... | 2204.00210 | title_snapshot |
bellows22_interspeech | Effect of Head Orientation on Speech Directivity | [
"Samuel Bellows",
"Timothy W. Leishman"
] | https://www.isca-archive.org/interspeech_2022/bellows22_interspeech.html | https://www.isca-archive.org/interspeech_2022/bellows22_interspeech.pdf | 10.21437/Interspeech.2022-553 | 246-250 | @inproceedings{bellows22_interspeech,
title = {{Effect of Head Orientation on Speech Directivity}},
author = {Samuel Bellows and Timothy W. Leishman},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {246--250},
doi = {10.21437/Interspeech.2022-553},
issn = {2958-1796},
... | The directional characteristics of human speech have many applications in speech acoustics, audio, telecommunications, room acoustical design, and other areas. However, professionals in these fields require carefully conducted, high-resolution, spherical speech directivity measurements taken under distinct circumstance... | null | null |
saijo22c_interspeech | Unsupervised Training of Sequential Neural Beamformer Using Coarsely-separated and Non-separated Signals | [
"Kohei Saijo",
"Tetsuji Ogawa"
] | https://www.isca-archive.org/interspeech_2022/saijo22c_interspeech.html | https://www.isca-archive.org/interspeech_2022/saijo22c_interspeech.pdf | 10.21437/Interspeech.2022-976 | 251-255 | @inproceedings{saijo22c_interspeech,
title = {{Unsupervised Training of Sequential Neural Beamformer Using Coarsely-separated and Non-separated Signals}},
author = {Kohei Saijo and Tetsuji Ogawa},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {251--255},
doi = {10.21437/Inte... | We present an unsupervised training method of the sequential neural beamformer (Seq-BF) using coarsely-separated and non-separated supervisory signals. The signal coarsely separated by blind source separation (BSS) has been used for training neural separators in an unsupervised manner. However, the performance is limit... | null | null |
borsdorf22_interspeech | Blind Language Separation: Disentangling Multilingual Cocktail Party Voices by Language | [
"Marvin Borsdorf",
"Kevin Scheck",
"Haizhou Li",
"Tanja Schultz"
] | https://www.isca-archive.org/interspeech_2022/borsdorf22_interspeech.html | https://www.isca-archive.org/interspeech_2022/borsdorf22_interspeech.pdf | 10.21437/Interspeech.2022-10187 | 256-260 | @inproceedings{borsdorf22_interspeech,
title = {{Blind Language Separation: Disentangling Multilingual Cocktail Party Voices by Language}},
author = {Marvin Borsdorf and Kevin Scheck and Haizhou Li and Tanja Schultz},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {256--260},
doi ... | We introduce blind language separation (BLS) as novel research task, in which we seek to disentangle overlapping voices of multiple languages by language. BLS is expected to separate seen as well as unseen languages, which is different from the target language extraction task that works for one seen target language at ... | null | null |
guzik22_interspeech | NTF of Spectral and Spatial Features for Tracking and Separation of Moving Sound Sources in Spherical Harmonic Domain | [
"Mateusz Guzik",
"Konrad Kowalczyk"
] | https://www.isca-archive.org/interspeech_2022/guzik22_interspeech.html | https://www.isca-archive.org/interspeech_2022/guzik22_interspeech.pdf | 10.21437/Interspeech.2022-10526 | 261-265 | @inproceedings{guzik22_interspeech,
title = {{NTF of Spectral and Spatial Features for Tracking and Separation of Moving Sound Sources in Spherical Harmonic Domain}},
author = {Mateusz Guzik and Konrad Kowalczyk},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {261--265},
doi ... | This paper presents a novel Non-negative Tensor Factorization (NTF) based approach to tracking and separation of moving sound sources, formulated in the Spherical Harmonic Domain (SHD). In particular, at first, we redefine an already existing Ambisonic NTF by introducing time-dependence into the Spatial Covariance Matr... | null | null |
deadman22_interspeech | Modelling Turn-taking in Multispeaker Parties for Realistic Data Simulation | [
"Jack Deadman",
"Jon Barker"
] | https://www.isca-archive.org/interspeech_2022/deadman22_interspeech.html | https://www.isca-archive.org/interspeech_2022/deadman22_interspeech.pdf | 10.21437/Interspeech.2022-10842 | 266-270 | @inproceedings{deadman22_interspeech,
title = {{Modelling Turn-taking in Multispeaker Parties for Realistic Data Simulation}},
author = {Jack Deadman and Jon Barker},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {266--270},
doi = {10.21437/Interspeech.2022-10842},
issn ... | Simulation plays a crucial role in developing components of automatic speech recognition systems such as enhancement and diarization. In source separation and target-speaker extraction, datasets with high degrees of temporal overlap are used both in training and evaluation. However, this contrasts with the fact that pe... | null | null |
boeddeker22_interspeech | An Initialization Scheme for Meeting Separation with Spatial Mixture Models | [
"Christoph Boeddeker",
"Tobias Cord-Landwehr",
"Thilo von Neumann",
"Reinhold Haeb-Umbach"
] | https://www.isca-archive.org/interspeech_2022/boeddeker22_interspeech.html | https://www.isca-archive.org/interspeech_2022/boeddeker22_interspeech.pdf | 10.21437/Interspeech.2022-10929 | 271-275 | @inproceedings{boeddeker22_interspeech,
title = {{An Initialization Scheme for Meeting Separation with Spatial Mixture Models}},
author = {Christoph Boeddeker and Tobias Cord-Landwehr and Thilo {von Neumann} and Reinhold Haeb-Umbach},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {2... | Spatial mixture model (SMM) supported acoustic beamforming has been extensively used for the separation of simultaneously active speakers. However, it has hardly been considered for the separation of meeting data, that are characterized by long recordings and only partially overlapping speech. In this contribution, we ... | 2204.01338 | title_snapshot |
mun22_interspeech | Prototypical speaker-interference loss for target voice separation using non-parallel audio samples | [
"Seongkyu Mun",
"Dhananjaya Gowda",
"Jihwan Lee",
"Changwoo Han",
"Dokyun Lee",
"Chanwoo Kim"
] | https://www.isca-archive.org/interspeech_2022/mun22_interspeech.html | https://www.isca-archive.org/interspeech_2022/mun22_interspeech.pdf | 10.21437/Interspeech.2022-11236 | 276-280 | @inproceedings{mun22_interspeech,
title = {{Prototypical speaker-interference loss for target voice separation using non-parallel audio samples}},
author = {Seongkyu Mun and Dhananjaya Gowda and Jihwan Lee and Changwoo Han and Dokyun Lee and Chanwoo Kim},
year = {2022},
booktitle = {{Interspeech 202... | In this paper, we propose a new prototypical loss function for training neural network models for target voice separation. Conventional methods use paired parallel audio samples of the target speaker with and without an interfering speaker or noise, and minimize the spectrographic mean squared error (MSE) between the c... | null | null |
bousquet22_interspeech | Reliability criterion based on learning-phase entropy for speaker recognition with neural network | [
"Pierre-Michel Bousquet",
"Mickael Rouvier",
"Jean-Francois Bonastre"
] | https://www.isca-archive.org/interspeech_2022/bousquet22_interspeech.html | https://www.isca-archive.org/interspeech_2022/bousquet22_interspeech.pdf | 10.21437/Interspeech.2022-8 | 281-285 | @inproceedings{bousquet22_interspeech,
title = {{Reliability criterion based on learning-phase entropy for speaker recognition with neural network}},
author = {Pierre-Michel Bousquet and Mickael Rouvier and Jean-Francois Bonastre},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {281-... | The reliability of Automatic Speaker Recognition (SR) is of the utmost importance for real-world applications. Even if SR systems obtain spectacular performance during evaluation campaigns, several studies have shown the limits and shortcomings of these systems. Reliability first means knowing where and when a system i... | null | null |
liu22f_interspeech | Attentive Feature Fusion for Robust Speaker Verification | [
"Bei Liu",
"Zhengyang Chen",
"Yanmin Qian"
] | https://www.isca-archive.org/interspeech_2022/liu22f_interspeech.html | https://www.isca-archive.org/interspeech_2022/liu22f_interspeech.pdf | 10.21437/Interspeech.2022-478 | 286-290 | @inproceedings{liu22f_interspeech,
title = {{Attentive Feature Fusion for Robust Speaker Verification}},
author = {Bei Liu and Zhengyang Chen and Yanmin Qian},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {286--290},
doi = {10.21437/Interspeech.2022-478},
issn = {295... | As the most widely used technique, deep speaker embedding learning has become predominant in speaker verification task recently. This approach utilizes deep neural networks to extract fixed dimension embedding vectors which represent different speaker identities. Two network architectures such as ResNet and ECAPA-TDNN ... | null | null |
liu22g_interspeech | Dual Path Embedding Learning for Speaker Verification with Triplet Attention | [
"Bei Liu",
"Zhengyang Chen",
"Yanmin Qian"
] | https://www.isca-archive.org/interspeech_2022/liu22g_interspeech.html | https://www.isca-archive.org/interspeech_2022/liu22g_interspeech.pdf | 10.21437/Interspeech.2022-481 | 291-295 | @inproceedings{liu22g_interspeech,
title = {{Dual Path Embedding Learning for Speaker Verification with Triplet Attention}},
author = {Bei Liu and Zhengyang Chen and Yanmin Qian},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {291--295},
doi = {10.21437/Interspeech.2022-481}... | Currently, many different network architectures have been explored in speaker verification, including time-delay neural network (TDNN), convolutional neural network (CNN), transformer and multi-layer perceptrons (MLP). However, hybrid networks with diverse structures are rarely investigated. In this paper, we present a... | null | null |
liu22h_interspeech | DF-ResNet: Boosting Speaker Verification Performance with Depth-First Design | [
"Bei Liu",
"Zhengyang Chen",
"Shuai Wang",
"Haoyu Wang",
"Bing Han",
"Yanmin Qian"
] | https://www.isca-archive.org/interspeech_2022/liu22h_interspeech.html | https://www.isca-archive.org/interspeech_2022/liu22h_interspeech.pdf | 10.21437/Interspeech.2022-484 | 296-300 | @inproceedings{liu22h_interspeech,
title = {{DF-ResNet: Boosting Speaker Verification Performance with Depth-First Design}},
author = {Bei Liu and Zhengyang Chen and Shuai Wang and Haoyu Wang and Bing Han and Yanmin Qian},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {296--300},
... | Embeddings extracted by deep neural networks have become the state-of-the-art utterance representation in speaker verification (SV). Despite the various network architectures that have been investigated in previous works, how to design and scale up networks to achieve a better trade-off on performance and complexity in... | null | null |
ruida22_interspeech | Adaptive Rectangle Loss for Speaker Verification | [
"Li Ruida",
"Fang Shuo",
"Ma Chenguang",
"Li Liang"
] | https://www.isca-archive.org/interspeech_2022/ruida22_interspeech.html | https://www.isca-archive.org/interspeech_2022/ruida22_interspeech.pdf | 10.21437/Interspeech.2022-486 | 301-305 | @inproceedings{ruida22_interspeech,
title = {{Adaptive Rectangle Loss for Speaker Verification}},
author = {Li Ruida and Fang Shuo and Ma Chenguang and Li Liang},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {301--305},
doi = {10.21437/Interspeech.2022-486},
issn = {... | From the perspective of pair similarity optimization, speaker verification is expected to satisfy the criterion that each intraclass similarity is higher than the maximal inter-class similarity. However, we find that most softmax-based losses are suboptimal which encourages each sample to have a higher target similarit... | null | null |
zhang22h_interspeech | MFA-Conformer: Multi-scale Feature Aggregation Conformer for Automatic Speaker Verification | [
"Yang Zhang",
"Zhiqiang Lv",
"Haibin Wu",
"Shanshan Zhang",
"Pengfei Hu",
"Zhiyong Wu",
"Hung-yi Lee",
"Helen Meng"
] | https://www.isca-archive.org/interspeech_2022/zhang22h_interspeech.html | https://www.isca-archive.org/interspeech_2022/zhang22h_interspeech.pdf | 10.21437/Interspeech.2022-563 | 306-310 | @inproceedings{zhang22h_interspeech,
title = {{MFA-Conformer: Multi-scale Feature Aggregation Conformer for Automatic Speaker Verification}},
author = {Yang Zhang and Zhiqiang Lv and Haibin Wu and Shanshan Zhang and Pengfei Hu and Zhiyong Wu and Hung-yi Lee and Helen Meng},
year = {2022},
booktitle ... | In this paper, we present Multi-scale Feature Aggregation Conformer (MFA-Conformer), an easy-to-implement, simple but effective backbone for automatic speaker verification based on the Convolution-augmented Transformer (Conformer). The architecture of the MFA-Conformer is inspired by recent state-of-the-art models in s... | 2203.15249 | title_snapshot |
zhang22j_interspeech | Enroll-Aware Attentive Statistics Pooling for Target Speaker Verification | [
"Leying Zhang",
"Zhengyang Chen",
"Yanmin Qian"
] | https://www.isca-archive.org/interspeech_2022/zhang22j_interspeech.html | https://www.isca-archive.org/interspeech_2022/zhang22j_interspeech.pdf | 10.21437/Interspeech.2022-645 | 311-315 | @inproceedings{zhang22j_interspeech,
title = {{Enroll-Aware Attentive Statistics Pooling for Target Speaker Verification}},
author = {Leying Zhang and Zhengyang Chen and Yanmin Qian},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {311--315},
doi = {10.21437/Interspeech.2022-... | The well-developed robust speaker verification system can remove the environment noise and retain speaker information automatically. However, when the uttering voice is disturbed by another interfering speaker's voice, the speaker verification system usually cannot selectively extract only the target speaker's informat... | null | null |
tian22b_interspeech | Transport-Oriented Feature Aggregation for Speaker Embedding Learning | [
"Yusheng Tian",
"Jingyu Li",
"Tan Lee"
] | https://www.isca-archive.org/interspeech_2022/tian22b_interspeech.html | https://www.isca-archive.org/interspeech_2022/tian22b_interspeech.pdf | 10.21437/Interspeech.2022-886 | 316-320 | @inproceedings{tian22b_interspeech,
title = {{Transport-Oriented Feature Aggregation for Speaker Embedding Learning}},
author = {Yusheng Tian and Jingyu Li and Tan Lee},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {316--320},
doi = {10.21437/Interspeech.2022-886},
issn ... | Pooling is needed to aggregate frame-level features into utterance-level representations for speaker modeling. Given the success of statistics-based pooling methods, we hypothesize that speaker characteristics are well represented in the statistical distribution over the pre-aggregation layer's output, and propose to u... | 2206.12857 | title_snapshot |
sang22_interspeech | Multi-Frequency Information Enhanced Channel Attention Module for Speaker Representation Learning | [
"Mufan Sang",
"John H.L. Hansen"
] | https://www.isca-archive.org/interspeech_2022/sang22_interspeech.html | https://www.isca-archive.org/interspeech_2022/sang22_interspeech.pdf | 10.21437/Interspeech.2022-892 | 321-325 | @inproceedings{sang22_interspeech,
title = {{Multi-Frequency Information Enhanced Channel Attention Module for Speaker Representation Learning}},
author = {Mufan Sang and John H.L. Hansen},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {321--325},
doi = {10.21437/Interspeech... | Recently, attention mechanisms have been applied successfully in neural network-based speaker verification systems. Incorporating the Squeeze-and-Excitation block into convolutional neural networks has achieved remarkable performance. However, it uses global average pooling (GAP) to simply average the features along ti... | 2207.04540 | title_snapshot |
cai22_interspeech | CS-CTCSCONV1D: Small footprint speaker verification with channel split time-channel-time separable 1-dimensional convolution | [
"Linjun Cai",
"Yuhong Yang",
"Xufeng Chen",
"Weiping Tu",
"Hongyang Chen"
] | https://www.isca-archive.org/interspeech_2022/cai22_interspeech.html | https://www.isca-archive.org/interspeech_2022/cai22_interspeech.pdf | 10.21437/Interspeech.2022-913 | 326-330 | @inproceedings{cai22_interspeech,
title = {{CS-CTCSCONV1D: Small footprint speaker verification with channel split time-channel-time separable 1-dimensional convolution}},
author = {Linjun Cai and Yuhong Yang and Xufeng Chen and Weiping Tu and Hongyang Chen},
year = {2022},
booktitle = {{Interspeech... | We present an efficient small-footprint network for speaker verification. We start by introducing the bottleneck to the QuartzNet model. Then we proposed a Channel Split Time Channel-Time Separable 1-dimensional Convolution (CS-CTCSConv1d) module, yielding stronger performance over the State-Of-The-Art small footprint ... | null | null |
li22l_interspeech | Reliable Visualization for Deep Speaker Recognition | [
"Pengqi Li",
"Lantian Li",
"Askar Hamdulla",
"Dong Wang"
] | https://www.isca-archive.org/interspeech_2022/li22l_interspeech.html | https://www.isca-archive.org/interspeech_2022/li22l_interspeech.pdf | 10.21437/Interspeech.2022-926 | 331-335 | @inproceedings{li22l_interspeech,
title = {{Reliable Visualization for Deep Speaker Recognition}},
author = {Pengqi Li and Lantian Li and Askar Hamdulla and Dong Wang},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {331--335},
doi = {10.21437/Interspeech.2022-926},
issn ... | In spite of the impressive success of convolutional neural networks (CNNs) in speaker recognition, our understanding to CNNs' internal functions is still limited. A major obstacle is that some popular visualization tools are difficult to apply, for example those producing saliency maps. The reason is that speaker infor... | 2204.03852 | title_snapshot |
peng22b_interspeech | Unifying Cosine and PLDA Back-ends for Speaker Verification | [
"Zhiyuan Peng",
"Xuanji He",
"Ke Ding",
"Tan Lee",
"Guanglu Wan"
] | https://www.isca-archive.org/interspeech_2022/peng22b_interspeech.html | https://www.isca-archive.org/interspeech_2022/peng22b_interspeech.pdf | 10.21437/Interspeech.2022-10021 | 336-340 | @inproceedings{peng22b_interspeech,
title = {{Unifying Cosine and PLDA Back-ends for Speaker Verification}},
author = {Zhiyuan Peng and Xuanji He and Ke Ding and Tan Lee and Guanglu Wan},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {336--340},
doi = {10.21437/Interspeech.2... | State-of-art speaker verification (SV) systems use a back-end model to score the similarity of speaker embeddings extracted from a neural network. The commonly used back-ends are the cosine scoring and the probabilistic linear discriminant analysis (PLDA) scoring. With the recently developed neural embeddings, the theo... | 2204.10523 | title_snapshot |
wei22d_interspeech | CTFALite: Lightweight Channel-specific Temporal and Frequency Attention Mechanism for Enhancing the Speaker Embedding Extractor | [
"Yuheng Wei",
"Junzhao Du",
"Hui Liu",
"Qian Wang"
] | https://www.isca-archive.org/interspeech_2022/wei22d_interspeech.html | https://www.isca-archive.org/interspeech_2022/wei22d_interspeech.pdf | 10.21437/Interspeech.2022-10288 | 341-345 | @inproceedings{wei22d_interspeech,
title = {{CTFALite: Lightweight Channel-specific Temporal and Frequency Attention Mechanism for Enhancing the Speaker Embedding Extractor}},
author = {Yuheng Wei and Junzhao Du and Hui Liu and Qian Wang},
year = {2022},
booktitle = {{Interspeech 2022}},
pages ... | Attention mechanism provides an effective and plug-and-play feature enhancement module for speaker embedding extractors. Attention-based pooling layers have been widely used to aggregate a sequence of frame-level feature vectors into an utterance-level speaker embedding. Besides, convolution attention mechanisms are in... | null | null |
chen22_interspeech | SpeechFormer: A Hierarchical Efficient Framework Incorporating the Characteristics of Speech | [
"Weidong Chen",
"Xiaofen Xing",
"Xiangmin Xu",
"Jianxin Pang",
"Lan Du"
] | https://www.isca-archive.org/interspeech_2022/chen22_interspeech.html | https://www.isca-archive.org/interspeech_2022/chen22_interspeech.pdf | 10.21437/Interspeech.2022-74 | 346-350 | @inproceedings{chen22_interspeech,
title = {{SpeechFormer: A Hierarchical Efficient Framework Incorporating the Characteristics of Speech}},
author = {Weidong Chen and Xiaofen Xing and Xiangmin Xu and Jianxin Pang and Lan Du},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {346--350}... | Transformer has obtained promising results on cognitive speech signal processing field, which is of interest in various applications ranging from emotion to neurocognitive disorder analysis. However, most works treat speech signal as a whole, leading to the neglect of the pronunciation structure that is unique to speec... | 2203.03812 | title_snapshot |
feinberg22_interspeech | VoiceLab: Software for Fully Reproducible Automated Voice Analysis | [
"David Feinberg"
] | https://www.isca-archive.org/interspeech_2022/feinberg22_interspeech.html | https://www.isca-archive.org/interspeech_2022/feinberg22_interspeech.pdf | 10.21437/Interspeech.2022-113 | 351-355 | @inproceedings{feinberg22_interspeech,
title = {{VoiceLab: Software for Fully Reproducible Automated Voice Analysis}},
author = {David Feinberg},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {351--355},
doi = {10.21437/Interspeech.2022-113},
issn = {2958-1796},
} | There's a problem with acoustic analyses because you often need to hand adjust parameters meaning you can only process them individually or in small batches. This creates two key problems. First, it compromises the reproducibility of measurements because setting parameters by hand requires specialist knowledge and is o... | null | null |
shor22_interspeech | TRILLsson: Distilled Universal Paralinguistic Speech Representations | [
"Joel Shor",
"Subhashini Venugopalan"
] | https://www.isca-archive.org/interspeech_2022/shor22_interspeech.html | https://www.isca-archive.org/interspeech_2022/shor22_interspeech.pdf | 10.21437/Interspeech.2022-118 | 356-360 | @inproceedings{shor22_interspeech,
title = {{TRILLsson: Distilled Universal Paralinguistic Speech Representations}},
author = {Joel Shor and Subhashini Venugopalan},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {356--360},
doi = {10.21437/Interspeech.2022-118},
issn ... | Recent advances in self-supervision have dramatically improved the quality of speech representations. However, deployment of state-of-the-art embedding models on devices has been restricted due to their limited public availability and large resource footprint. Our work addresses these issues by publicly releasing a col... | 2203.00236 | title_snapshot |
li22b_interspeech | Global Signal-to-noise Ratio Estimation Based on Multi-subband Processing Using Convolutional Neural Network | [
"Nan LI",
"Meng Ge",
"Longbiao Wang",
"Masashi Unoki",
"Sheng Li",
"Jianwu Dang"
] | https://www.isca-archive.org/interspeech_2022/li22b_interspeech.html | https://www.isca-archive.org/interspeech_2022/li22b_interspeech.pdf | 10.21437/Interspeech.2022-154 | 361-365 | @inproceedings{li22b_interspeech,
title = {{Global Signal-to-noise Ratio Estimation Based on Multi-subband Processing Using Convolutional Neural Network}},
author = {Nan LI and Meng Ge and Longbiao Wang and Masashi Unoki and Sheng Li and Jianwu Dang},
year = {2022},
booktitle = {{Interspeech 2022}},... | The global signal-to-noise ratio (gSNR) is defined as the ratio of speech energy to noise energy in whole noisy audio. However, due to the increase in noise interference, the generalization ability declines when the traditional features (e.g., raw waveforms and MFCCs) are fed directly to the statistical model to estima... | null | null |
sadeghi22_interspeech | A Sparsity-promoting Dictionary Model for Variational Autoencoders | [
"Mostafa Sadeghi",
"Paul Magron"
] | https://www.isca-archive.org/interspeech_2022/sadeghi22_interspeech.html | https://www.isca-archive.org/interspeech_2022/sadeghi22_interspeech.pdf | 10.21437/Interspeech.2022-237 | 366-370 | @inproceedings{sadeghi22_interspeech,
title = {{A Sparsity-promoting Dictionary Model for Variational Autoencoders}},
author = {Mostafa Sadeghi and Paul Magron},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {366--370},
doi = {10.21437/Interspeech.2022-237},
issn = {2... | Structuring the latent space in probabilistic deep generative models, e.g., variational autoencoders (VAEs), is important to yield more expressive models and interpretable representations, and to avoid overfitting. One way to achieve this objective is to impose a sparsity constraint on the latent variables, e.g., via a... | 2203.15758 | title_snapshot |
zhao22h_interspeech | Deep Transductive Transfer Regression Network for Cross-Corpus Speech Emotion Recognition | [
"Yan Zhao",
"Jincen Wang",
"Ru Ye",
"Yuan Zong",
"Wenming Zheng",
"Li Zhao"
] | https://www.isca-archive.org/interspeech_2022/zhao22h_interspeech.html | https://www.isca-archive.org/interspeech_2022/zhao22h_interspeech.pdf | 10.21437/Interspeech.2022-679 | 371-375 | @inproceedings{zhao22h_interspeech,
title = {{Deep Transductive Transfer Regression Network for Cross-Corpus Speech Emotion Recognition}},
author = {Yan Zhao and Jincen Wang and Ru Ye and Yuan Zong and Wenming Zheng and Li Zhao},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {371--3... | In this paper, we focus on the research of cross-corpus speech emotion recognition (SER), in which the training (source) and testing (target) speech samples come from different corpora leading to a feature distribution gap between them. To solve this problem, we propose a simple yet effective method called deep transdu... | null | null |
hansen22_interspeech | Audio Anti-spoofing Using Simple Attention Module and Joint Optimization Based on Additive Angular Margin Loss and Meta-learning | [
"John H.L. Hansen",
"ZHENYU WANG"
] | https://www.isca-archive.org/interspeech_2022/hansen22_interspeech.html | https://www.isca-archive.org/interspeech_2022/hansen22_interspeech.pdf | 10.21437/Interspeech.2022-904 | 376-380 | @inproceedings{hansen22_interspeech,
title = {{Audio Anti-spoofing Using Simple Attention Module and Joint Optimization Based on Additive Angular Margin Loss and Meta-learning}},
author = {John H.L. Hansen and ZHENYU WANG},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {376--380},
... | Automatic speaker verification systems are vulnerable to a variety of access threats, prompting research into the formulation of effective spoofing detection systems to act as a gate to filter out such spoofing attacks. This study introduces a simple attention module to infer 3-dim attention weights for the feature map... | 2211.09898 | title_judge |
bergsma22_interspeech | PEAF: Learnable Power Efficient Analog Acoustic Features for Audio Recognition | [
"Boris Bergsma",
"Minhao Yang",
"Milos Cernak"
] | https://www.isca-archive.org/interspeech_2022/bergsma22_interspeech.html | https://www.isca-archive.org/interspeech_2022/bergsma22_interspeech.pdf | 10.21437/Interspeech.2022-10412 | 381-385 | @inproceedings{bergsma22_interspeech,
title = {{PEAF: Learnable Power Efficient Analog Acoustic Features for Audio Recognition}},
author = {Boris Bergsma and Minhao Yang and Milos Cernak},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {381--385},
doi = {10.21437/Interspeech.... | At the end of Mooreâs law, new computing paradigms are required to prolong the battery life of wearable and IoT smart audio devices. Theoretical analysis and physical validation have shown that analog signal processing (ASP) can be more power-efficient than its digital counterpart in the realm of low-to-medium signal... | 2110.03715 | title_snapshot |
elbanna22_interspeech | Hybrid Handcrafted and Learnable Audio Representation for Analysis of Speech Under Cognitive and Physical Load | [
"Gasser Elbanna",
"Alice Biryukov",
"Neil Scheidwasser-Clow",
"Lara Orlandic",
"Pablo Mainar",
"Mikolaj Kegler",
"Pierre Beckmann",
"Milos Cernak"
] | https://www.isca-archive.org/interspeech_2022/elbanna22_interspeech.html | https://www.isca-archive.org/interspeech_2022/elbanna22_interspeech.pdf | 10.21437/Interspeech.2022-10498 | 386-390 | @inproceedings{elbanna22_interspeech,
title = {{Hybrid Handcrafted and Learnable Audio Representation for Analysis of Speech Under Cognitive and Physical Load}},
author = {Gasser Elbanna and Alice Biryukov and Neil Scheidwasser-Clow and Lara Orlandic and Pablo Mainar and Mikolaj Kegler and Pierre Beckmann an... | As a neurophysiological response to threat or adverse conditions, stress can affect cognition, emotion and behaviour with potentially detrimental effects on health in the case of sustained exposure. Since the affective content of speech is inherently modulated by an individual's physical and mental state, a substantial... | 2203.16637 | title_snapshot |
wang22w_interspeech | Generative Data Augmentation Guided by Triplet Loss for Speech Emotion Recognition | [
"Shijun Wang",
"Hamed Hemati",
"Jón Guðnason",
"Damian Borth"
] | https://www.isca-archive.org/interspeech_2022/wang22w_interspeech.html | https://www.isca-archive.org/interspeech_2022/wang22w_interspeech.pdf | 10.21437/Interspeech.2022-10667 | 391-395 | @inproceedings{wang22w_interspeech,
title = {{Generative Data Augmentation Guided by Triplet Loss for Speech Emotion Recognition}},
author = {Shijun Wang and Hamed Hemati and Jón Guðnason and Damian Borth},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {391--395},
doi = {1... | Speech Emotion Recognition (SER) is crucial for human-computer interaction but still remains a challenging problem because of two major obstacles: data scarcity and imbalance. Many datasets for SER are substantially imbalanced, where data utterances of one class (most often Neutral) are much more frequent than those of... | 2208.04994 | title_snapshot |
yadav22_interspeech | Learning neural audio features without supervision | [
"Sarthak Yadav",
"Neil Zeghidour"
] | https://www.isca-archive.org/interspeech_2022/yadav22_interspeech.html | https://www.isca-archive.org/interspeech_2022/yadav22_interspeech.pdf | 10.21437/Interspeech.2022-10834 | 396-400 | @inproceedings{yadav22_interspeech,
title = {{Learning neural audio features without supervision}},
author = {Sarthak Yadav and Neil Zeghidour},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {396--400},
doi = {10.21437/Interspeech.2022-10834},
issn = {2958-1796},
} | Deep audio classification, traditionally cast as training a deep neural network on top of mel-filterbanks in a supervised fashion, has recently benefited from two independent lines of work. The first one explores "learnable frontends'', i.e., neural modules that produce a learnable time-frequency representation, to ove... | 2203.15519 | title_snapshot |
zhang22ca_interspeech | Densely-connected Convolutional Recurrent Network for Fundamental Frequency Estimation in Noisy Speech | [
"Yixuan Zhang",
"Heming Wang",
"DeLiang Wang"
] | https://www.isca-archive.org/interspeech_2022/zhang22ca_interspeech.html | https://www.isca-archive.org/interspeech_2022/zhang22ca_interspeech.pdf | 10.21437/Interspeech.2022-11156 | 401-405 | @inproceedings{zhang22ca_interspeech,
title = {{Densely-connected Convolutional Recurrent Network for Fundamental Frequency Estimation in Noisy Speech}},
author = {Yixuan Zhang and Heming Wang and DeLiang Wang},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {401--405},
doi =... | Estimating fundamental frequency (F0) from an audio signal is a necessary step in many tasks such as speech synthesis and speech analysis. Although high estimation accuracy has been achieved for clean speech, it is still challenging for F0 estimation to handle noisy speech, mainly because of the corruption of harmonic ... | null | null |
faridee22_interspeech | Predicting label distribution improves non-intrusive speech quality estimation | [
"Abu Zaher Md Faridee",
"Hannes Gamper"
] | https://www.isca-archive.org/interspeech_2022/faridee22_interspeech.html | https://www.isca-archive.org/interspeech_2022/faridee22_interspeech.pdf | 10.21437/Interspeech.2022-11186 | 406-410 | @inproceedings{faridee22_interspeech,
title = {{Predicting label distribution improves non-intrusive speech quality estimation}},
author = {Abu Zaher Md Faridee and Hannes Gamper},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {406--410},
doi = {10.21437/Interspeech.2022-111... | Deep noise suppressors (DNS) have become an attractive solution to remove background noise, reverberation, and distortions from speech and are widely used in telephony/voice applications. They are also occasionally prone to introducing artifacts and lowering the perceptual quality of the speech. Subjective listening te... | 2204.06616 | title_judge |
ashihara22_interspeech | Deep versus Wide: An Analysis of Student Architectures for Task-Agnostic Knowledge Distillation of Self-Supervised Speech Models | [
"Takanori Ashihara",
"Takafumi Moriya",
"Kohei Matsuura",
"Tomohiro Tanaka"
] | https://www.isca-archive.org/interspeech_2022/ashihara22_interspeech.html | https://www.isca-archive.org/interspeech_2022/ashihara22_interspeech.pdf | 10.21437/Interspeech.2022-11313 | 411-415 | @inproceedings{ashihara22_interspeech,
title = {{Deep versus Wide: An Analysis of Student Architectures for Task-Agnostic Knowledge Distillation of Self-Supervised Speech Models}},
author = {Takanori Ashihara and Takafumi Moriya and Kohei Matsuura and Tomohiro Tanaka},
year = {2022},
booktitle = {{I... | Self-supervised learning (SSL) is seen as a very promising approach with high performance for several speech downstream tasks. Since the parameters of SSL models are generally so large that training and inference require a lot of memory and computational cost, it is desirable to produce compact SSL models without a sig... | 2207.06867 | title_snapshot |
azeemi22_interspeech | Dataset Pruning for Resource-constrained Spoofed Audio Detection | [
"Abdul Hameed Azeemi",
"Ihsan Ayyub Qazi",
"Agha Ali Raza"
] | https://www.isca-archive.org/interspeech_2022/azeemi22_interspeech.html | https://www.isca-archive.org/interspeech_2022/azeemi22_interspeech.pdf | 10.21437/Interspeech.2022-514 | 416-420 | @inproceedings{azeemi22_interspeech,
title = {{Dataset Pruning for Resource-constrained Spoofed Audio Detection}},
author = {Abdul Hameed Azeemi and Ihsan Ayyub Qazi and Agha Ali Raza},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {416--420},
doi = {10.21437/Interspeech.202... | The performance of neural anti-spoofing models has rapidly improved in recent years due to larger network architectures and better training methodologies. However, these systems require considerable training data for achieving high performance, which makes it challenging to train them in compute-restricted environments... | null | null |
tae22_interspeech | EdiTTS: Score-based Editing for Controllable Text-to-Speech | [
"Jaesung Tae",
"Hyeongju Kim",
"Taesu Kim"
] | https://www.isca-archive.org/interspeech_2022/tae22_interspeech.html | https://www.isca-archive.org/interspeech_2022/tae22_interspeech.pdf | 10.21437/Interspeech.2022-6 | 421-425 | @inproceedings{tae22_interspeech,
title = {{EdiTTS: Score-based Editing for Controllable Text-to-Speech}},
author = {Jaesung Tae and Hyeongju Kim and Taesu Kim},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {421--425},
doi = {10.21437/Interspeech.2022-6},
issn = {295... | We present EdiTTS, an off-the-shelf speech editing methodology based on score-based generative modeling for text-to-speech synthesis. EdiTTS allows for targeted, granular editing of audio, both in terms of content and pitch, without the need for any additional training, task-specific optimization, or architectural modi... | 2110.02584 | title_snapshot |
chen22b_interspeech | Improving Mandarin Prosodic Structure Prediction with Multi-level Contextual Information | [
"Jie Chen",
"Changhe Song",
"Deyi Tuo",
"Xixin Wu",
"Shiyin Kang",
"Zhiyong Wu",
"Helen Meng"
] | https://www.isca-archive.org/interspeech_2022/chen22b_interspeech.html | https://www.isca-archive.org/interspeech_2022/chen22b_interspeech.pdf | 10.21437/Interspeech.2022-131 | 426-430 | @inproceedings{chen22b_interspeech,
title = {{Improving Mandarin Prosodic Structure Prediction with Multi-level Contextual Information}},
author = {Jie Chen and Changhe Song and Deyi Tuo and Xixin Wu and Shiyin Kang and Zhiyong Wu and Helen Meng},
year = {2022},
booktitle = {{Interspeech 2022}},
p... | For text-to-speech (TTS) synthesis, prosodic structure prediction (PSP) plays an important role in producing natural and intelligible speech. Although inter-utterance linguistic information can influence the speech interpretation of the target utterance, previous works on PSP mainly focus on utilizing intrautterance li... | 2308.16577 | title_snapshot |
borsos22_interspeech | SpeechPainter: Text-conditioned Speech Inpainting | [
"Zalan Borsos",
"Matthew Sharifi",
"Marco Tagliasacchi"
] | https://www.isca-archive.org/interspeech_2022/borsos22_interspeech.html | https://www.isca-archive.org/interspeech_2022/borsos22_interspeech.pdf | 10.21437/Interspeech.2022-194 | 431-435 | @inproceedings{borsos22_interspeech,
title = {{SpeechPainter: Text-conditioned Speech Inpainting}},
author = {Zalan Borsos and Matthew Sharifi and Marco Tagliasacchi},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {431--435},
doi = {10.21437/Interspeech.2022-194},
issn ... | We propose SpeechPainter, a model for filling in gaps of up to one second in speech samples by leveraging an auxiliary textual input. We demonstrate that the model performs speech inpainting with the appropriate content, while maintaining speaker identity, prosody and recording environment conditions, and generalizing ... | 2202.07273 | title_snapshot |
zhang22b_interspeech | A polyphone BERT for Polyphone Disambiguation in Mandarin Chinese | [
"Song Zhang",
"Ken Zheng",
"Xiaoxu Zhu",
"Baoxiang Li"
] | https://www.isca-archive.org/interspeech_2022/zhang22b_interspeech.html | https://www.isca-archive.org/interspeech_2022/zhang22b_interspeech.pdf | 10.21437/Interspeech.2022-229 | 436-440 | @inproceedings{zhang22b_interspeech,
title = {{A polyphone BERT for Polyphone Disambiguation in Mandarin Chinese}},
author = {Song Zhang and Ken Zheng and Xiaoxu Zhu and Baoxiang Li},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {436--440},
doi = {10.21437/Interspeech.2022-... | Grapheme-to-phoneme (G2P) conversion is an indispensable part of the Chinese Mandarin text-to-speech (TTS) system, and the core of G2P conversion is to solve the problem of polyphone disambiguation, which is to pick up the correct pronunciation for several candidates for a Chinese polyphonic character. In this paper, w... | 2207.12089 | title_snapshot |
he22b_interspeech | Neural Lexicon Reader: Reduce Pronunciation Errors in End-to-end TTS by Leveraging External Textual Knowledge | [
"Mutian He",
"Jingzhou Yang",
"Lei He",
"Frank Soong"
] | https://www.isca-archive.org/interspeech_2022/he22b_interspeech.html | https://www.isca-archive.org/interspeech_2022/he22b_interspeech.pdf | 10.21437/Interspeech.2022-420 | 441-445 | @inproceedings{he22b_interspeech,
title = {{Neural Lexicon Reader: Reduce Pronunciation Errors in End-to-end TTS by Leveraging External Textual Knowledge}},
author = {Mutian He and Jingzhou Yang and Lei He and Frank Soong},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {441--445},
... | End-to-end TTS requires a large amount of speech/text paired data to cover all necessary knowledge, particularly how to pronounce different words in diverse contexts, so that a neural model may learn such knowledge accordingly. But in real applications, such high demand of training data is hard to be satisfied and addi... | 2110.09698 | title_snapshot |
zhu22_interspeech | ByT5 model for massively multilingual grapheme-to-phoneme conversion | [
"Jian Zhu",
"Cong Zhang",
"David Jurgens"
] | https://www.isca-archive.org/interspeech_2022/zhu22_interspeech.html | https://www.isca-archive.org/interspeech_2022/zhu22_interspeech.pdf | 10.21437/Interspeech.2022-538 | 446-450 | @inproceedings{zhu22_interspeech,
title = {{ByT5 model for massively multilingual grapheme-to-phoneme conversion}},
author = {Jian Zhu and Cong Zhang and David Jurgens},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {446--450},
doi = {10.21437/Interspeech.2022-538},
issn ... | In this study, we tackle massively multilingual grapheme-to-phoneme conversion through implementing G2P models based on ByT5. We have curated a G2P dataset from various sources that covers around 100 languages and trained large-scale multilingual G2P models based on ByT5. We found that ByT5 operating on byte-level inpu... | 2204.03067 | title_snapshot |
mathur22_interspeech | DocLayoutTTS: Dataset and Baselines for Layout-informed Document-level Neural Speech Synthesis | [
"Puneet Mathur",
"Franck Dernoncourt",
"Quan Hung Tran",
"Jiuxiang Gu",
"Ani Nenkova",
"Vlad Morariu",
"Rajiv Jain",
"Dinesh Manocha"
] | https://www.isca-archive.org/interspeech_2022/mathur22_interspeech.html | https://www.isca-archive.org/interspeech_2022/mathur22_interspeech.pdf | 10.21437/Interspeech.2022-574 | 451-455 | @inproceedings{mathur22_interspeech,
title = {{DocLayoutTTS: Dataset and Baselines for Layout-informed Document-level Neural Speech Synthesis}},
author = {Puneet Mathur and Franck Dernoncourt and Quan Hung Tran and Jiuxiang Gu and Ani Nenkova and Vlad Morariu and Rajiv Jain and Dinesh Manocha},
year =... | We propose a new task of synthesizing speech directly from semi-structured documents where the extracted text tokens from OCR systems may not be in the correct reading order due to the complex document layout. We refer to this task as layout-informed document-level TTS and present the DocSpeech dataset which consists o... | null | null |
zhang22i_interspeech | Mixed-Phoneme BERT: Improving BERT with Mixed Phoneme and Sup-Phoneme Representations for Text to Speech | [
"Guangyan Zhang",
"Kaitao Song",
"Xu Tan",
"Daxin Tan",
"Yuzi Yan",
"Yanqing Liu",
"Gang Wang",
"Wei Zhou",
"Tao Qin",
"Tan Lee",
"Sheng Zhao"
] | https://www.isca-archive.org/interspeech_2022/zhang22i_interspeech.html | https://www.isca-archive.org/interspeech_2022/zhang22i_interspeech.pdf | 10.21437/Interspeech.2022-621 | 456-460 | @inproceedings{zhang22i_interspeech,
title = {{Mixed-Phoneme BERT: Improving BERT with Mixed Phoneme and Sup-Phoneme Representations for Text to Speech}},
author = {Guangyan Zhang and Kaitao Song and Xu Tan and Daxin Tan and Yuzi Yan and Yanqing Liu and Gang Wang and Wei Zhou and Tao Qin and Tan Lee and Shen... | Recently, leveraging BERT pre-training to improve the phoneme encoder in text to speech (TTS) has drawn increasing attention. However, the works apply pre-training with character-based units to enhance the TTS phoneme encoder, which is inconsistent with the TTS fine-tuning that takes phonemes as input. Pre-training onl... | 2203.17190 | title_snapshot |
ni22_interspeech | Unsupervised Text-to-Speech Synthesis by Unsupervised Automatic Speech Recognition | [
"Junrui Ni",
"Liming Wang",
"Heting Gao",
"Kaizhi Qian",
"Yang Zhang",
"Shiyu Chang",
"Mark Hasegawa-Johnson"
] | https://www.isca-archive.org/interspeech_2022/ni22_interspeech.html | https://www.isca-archive.org/interspeech_2022/ni22_interspeech.pdf | 10.21437/Interspeech.2022-816 | 461-465 | @inproceedings{ni22_interspeech,
title = {{Unsupervised Text-to-Speech Synthesis by Unsupervised Automatic Speech Recognition}},
author = {Junrui Ni and Liming Wang and Heting Gao and Kaizhi Qian and Yang Zhang and Shiyu Chang and Mark Hasegawa-Johnson},
year = {2022},
booktitle = {{Interspeech 2022... | An unsupervised text-to-speech synthesis (TTS) system learns to generate speech waveforms corresponding to any written sentence in a language by observing: 1) a collection of untranscribed speech waveforms in that language; 2) a collection of texts written in that language without access to any transcribed speech. Deve... | 2203.15796 | title_snapshot |
tran22_interspeech | An Efficient and High Fidelity Vietnamese Streaming End-to-End Speech Synthesis | [
"Tho Nguyen Duc Tran",
"The Chuong Chu",
"Vu Hoang",
"Trung Huu Bui",
"Hung Quoc Truong"
] | https://www.isca-archive.org/interspeech_2022/tran22_interspeech.html | https://www.isca-archive.org/interspeech_2022/tran22_interspeech.pdf | 10.21437/Interspeech.2022-922 | 466-470 | @inproceedings{tran22_interspeech,
title = {{An Efficient and High Fidelity Vietnamese Streaming End-to-End Speech Synthesis}},
author = {Tho Nguyen Duc Tran and The Chuong Chu and Vu Hoang and Trung Huu Bui and Hung Quoc Truong},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {466--... | In recent years, parallel end-to-end speech synthesis systems have outperformed the 2-stage TTS approaches in audio quality and latency. A parallel end-to-end speech like VITS can generate the audio with high MOS comparable to ground truth and achieve low latency on GPU. However, the VITS still has high latency when sy... | null | null |
valentinibotinhao22_interspeech | Predicting pairwise preferences between TTS audio stimuli using parallel ratings data and anti-symmetric twin neural networks | [
"Cassia Valentini-Botinhao",
"Manuel Sam Ribeiro",
"Oliver Watts",
"Korin Richmond",
"Gustav Eje Henter"
] | https://www.isca-archive.org/interspeech_2022/valentinibotinhao22_interspeech.html | https://www.isca-archive.org/interspeech_2022/valentinibotinhao22_interspeech.pdf | 10.21437/Interspeech.2022-10132 | 471-475 | @inproceedings{valentinibotinhao22_interspeech,
title = {{Predicting pairwise preferences between TTS audio stimuli using parallel ratings data and anti-symmetric twin neural networks}},
author = {Cassia Valentini-Botinhao and Manuel Sam Ribeiro and Oliver Watts and Korin Richmond and Gustav Eje Henter},
y... | Automatically predicting the outcome of subjective listening tests is a challenging task. Ratings may vary from person to person even if preferences are consistent across listeners. While previous work has focused on predicting listeners' ratings (mean opinion scores) of individual stimuli, we focus on the simpler task... | 2209.11003 | title_snapshot |
chen22j_interspeech | An Automatic Soundtracking System for Text-to-Speech Audiobooks | [
"Zikai Chen",
"Lin Wu",
"Junjie Pan",
"Xiang Yin"
] | https://www.isca-archive.org/interspeech_2022/chen22j_interspeech.html | https://www.isca-archive.org/interspeech_2022/chen22j_interspeech.pdf | 10.21437/Interspeech.2022-10236 | 476-480 | @inproceedings{chen22j_interspeech,
title = {{An Automatic Soundtracking System for Text-to-Speech Audiobooks}},
author = {Zikai Chen and Lin Wu and Junjie Pan and Xiang Yin},
year = {2022},
booktitle = {{Interspeech 2022}},
pages = {476--480},
doi = {10.21437/Interspeech.2022-10236},
... | Background music (BGM) plays an essential role in audiobooks, which can enhance the immersive experience of audiences and help them better understand the story. However, well-designed BGM still requires human effort in the text-to-speech (TTS) audiobook production, which is quite time-consuming and costly. In this pape... | null | null |
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