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technologyreview.com·1h ago
AI Research
arXiv CS.AI·Jul 1·5 min read
UniSAE: Unified Speech Attribute Editing on Speaker, Emotion and Low-Level Content via Discrete Phonetic Posteriorgram Modelling
TL;DR
Existing speech editing methods often struggle to simultaneously modify multiple attributes like speaker identity, emotion, and low-level content. UniSAE, a new tool based on Discrete Phonetic Posteriorgram (DPPG) modeling, enables unified editing of these attributes in speech.
✦ Why It Matters
Engineers can leverage UniSAE to develop advanced speech applications that require nuanced control over multiple speech attributes.
Key Takeaways
- Utilize UniSAE for advanced speech editing tasks.
- Implement DPPG for detailed phoneme-level modifications.
- Leverage autoregressive transformers for content prediction.
- Explore joint editing of speaker, emotion, and content.
How It Works
UniSAE employs a Discrete Phonetic PosteriorGram (DPPG) to represent speech, allowing for granular editing at the phoneme level. The DPPG encodes phoneme identity, pronunciation variants, and duration as discrete tokens.
An autoregressive content transformer predicts sequences for higher-level edits, while a diffusion-based acoustic decoder synthesizes the final speech output, conditioned on speaker and emotion attributes.