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AI Research
arXiv CS.AI·Jun 9·5 min read
GenTSE: Enhancing Target Speaker Extraction via a Coarse-to-Fine Generative Language Model
TL;DR
Target Speaker Extraction (TSE) struggles with accurately isolating a specific speaker's voice from mixed audio. GenTSE is a two-stage generative language model that first predicts coarse semantic tokens and then generates fine acoustic tokens, improving the stability and accuracy of speech decoding.
✦ Why It Matters
Engineers can leverage GenTSE to improve speaker isolation in audio applications, enhancing voice recognition systems.
Key Takeaways
- Utilize GenTSE for improved speaker extraction in audio applications.
- Leverage continuous SSL embeddings for richer context in models.
- Apply Frozen-LM Conditioning to reduce exposure bias in training.
- Experiment with DPO for better alignment with perceptual preferences.
How It Works
GenTSE operates in two stages: the first stage generates coarse semantic tokens that capture the meaning of the speech, while the second stage refines these into fine acoustic tokens that represent the actual sound. This two-step approach allows for more stable and accurate decoding, as it separates the understanding of content from the generation of sound.
Continuous SSL embeddings are used to provide a richer context, enhancing the model's performance compared to traditional discretized methods.