Unlocking Fine-Grained and Within-Utterance Speaking Style Control in Prompt-Based Text-to-Speech Models
arXiv:2605.27376v1 Announce Type: cross Abstract: While prompt-based text-to-speech (TTS) models enable natural language-driven speaking style control, they often provide limited fine-grained control and apply a single global style across an utterance. This restricts practical use cases that require continuous style attribute interpolation across utterances and time-varying style transitions within a single utterance. In this paper, we propose novel techniques to achieve both capabilities in existing prompt-based TTS models. For inter-utterance style interpolation, we compute direction vectors
The rapid advancement of prompt-based AI models in numerous domains is driving innovation towards more granular and controllable outputs, making this a natural next step in text-to-speech development.
This breakthrough offers significantly more nuanced and natural-sounding AI speech, which is critical for realistic human-computer interaction, content creation, and accessibility, moving beyond generic AI voices.
Previously static or globally applied speaking styles in AI-generated speech can now be dynamically altered within a single utterance, allowing for fine-grained expressive control that mimics human speech patterns.
- · Content creators
- · AI voice actors
- · Accessibility technology providers
- · AI developers
- · Generic TTS providers
- · Voice-over artists (for basic tasks)
AI-generated audio content will become indistinguishable from human-spoken content in terms of style and expression.
The demand for human voice actors for synthetic voice generation will increase, but their roles will shift towards creating style libraries rather than full performances.
This could lead to new forms of immersive media where AI dynamically adjusts character voices based on real-time emotional cues, blurring the lines of authorship.
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Read at arXiv cs.AI