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technologyreview.com·3h ago
AI Research
arXiv CS.AI·May 28·5 min read
Unlocking Fine-Grained and Within-Utterance Speaking Style Control in Prompt-Based Text-to-Speech Models
TL;DR
Text-to-speech (TTS) models traditionally lack nuanced control over speaking styles within a single utterance. This research introduces a method for fine-grained control of speaking styles in prompt-based TTS models, allowing users to specify styles at a detailed level.
✦ Why It Matters
Engineers can implement fine-grained style control in TTS applications to enhance user engagement and satisfaction.
Key Takeaways
- Utilize direction vectors for smooth style transitions between utterances.
- Implement KV-cache swapping to improve audio generation quality.
- Achieve significant pitch and speed variations in speech synthesis.
- Maintain high speaker similarity and perceptual smoothness in generated speech.
How It Works
The authors compute direction vectors between different style prompts in the embedding space, allowing for smooth interpolation of styles. They also address the issue of early tokens dominating the output by using KV-cache swapping and sliding-window attention masking, which helps balance the influence of all tokens during audio generation.