DSA-Tokenizer: Disentangled Semantic-Acoustic Tokenization via Flow Matching-based Hierarchical Fusion
📰 ArXiv cs.AI
Learn how DSA-Tokenizer achieves disentangled semantic-acoustic tokenization for speech LLMs, enabling better representation of speech signals
Action Steps
- Implement DSA-Tokenizer using PyTorch or TensorFlow to disentangle semantic and acoustic tokens
- Apply flow matching-based hierarchical fusion to fuse semantic and acoustic features
- Configure distinct optimization constraints for semantic and acoustic tokenization
- Test DSA-Tokenizer on speech datasets to evaluate its performance
- Compare the results with existing tokenization methods to assess its effectiveness
Who Needs to Know This
NLP engineers and researchers working on speech LLMs can benefit from this article to improve their tokenization techniques
Key Insight
💡 Disentangled semantic-acoustic tokenization can improve the representation of speech signals in LLMs
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Introducing DSA-Tokenizer: a novel approach to disentangled semantic-acoustic tokenization for speech LLMs #NLP #SpeechLLMs
Key Takeaways
Learn how DSA-Tokenizer achieves disentangled semantic-acoustic tokenization for speech LLMs, enabling better representation of speech signals
Full Article
Title: DSA-Tokenizer: Disentangled Semantic-Acoustic Tokenization via Flow Matching-based Hierarchical Fusion
Abstract:
arXiv:2601.09239v3 Announce Type: replace-cross Abstract: Speech tokenizers are a key building block of fully discrete Speech LLMs. Existing tokenizers either prioritize semantic encoding, fuse semantic content with acoustic style inseparably, or achieve incomplete semantic-acoustic disentanglement. To achieve better disentanglement, we propose \textbf{DSA-Tokenizer}, which explicitly disentangles speech into discrete semantic and acoustic tokens via distinct optimization constraints. Specifical
Abstract:
arXiv:2601.09239v3 Announce Type: replace-cross Abstract: Speech tokenizers are a key building block of fully discrete Speech LLMs. Existing tokenizers either prioritize semantic encoding, fuse semantic content with acoustic style inseparably, or achieve incomplete semantic-acoustic disentanglement. To achieve better disentanglement, we propose \textbf{DSA-Tokenizer}, which explicitly disentangles speech into discrete semantic and acoustic tokens via distinct optimization constraints. Specifical
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