Keyword Transformer: A Self-Attention Model for Keyword Spotting
Paper • 2104.00769 • Published
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SoundSense AI - KWT-Tiny Keyword Spotter
This is a fine-tuned/adapted model checkpoint for SoundSense AI.
Base model / source:
- Keyword Transformer (KWT), https://arxiv.org/abs/2104.00769
- Custom KWT-Tiny implementation (12-layer transformer, 64-dim embeddings)
Training data:
- Google Speech Commands v2 (35 keyword classes + silence + unknown, 37 total classes)
Use:
- Part of SoundSense AI hackathon submission (Stage 5: Keyword Spotting).
- Runs with shared weights across all 3 separated speaker streams for simultaneous multi-user command word detection, at zero additional parameter cost.
Limitations:
- Built for prototype/demo use.
- Trained on isolated English keyword clips, not continuous Hinglish speech or custom wake phrases (e.g. "hey jarvis"). Best suited for single-word command detection within an already-separated, already-VAD-gated audio segment.
- Performance should be verified on the target environment before deployment.
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