arXiv:2606.05889v1 Announce Type: cross Abstract: We propose GLASS, a framework for composable acoustic style control in zero-shot autoregressive text-to-speech (TTS) that learns controls from post-generation rewards rather than style labels. In zero-shot TTS, a speaker prompt often entangles speaker identity with prosodic attributes such as speaking rate and pitch, making it difficult to change style without changing the prompt itself. GLASS instead treats each acoustic attribute as a reward-defined control direction. For each control axis, GLASS freezes the TTS backbone and trains one lightw
The rapid advancements in large language models and generative AI are pushing the boundaries of speech synthesis, making more nuanced control over acoustic properties a next logical step for improved realism and application usability.
This breakthrough represents a significant step towards highly controllable and realistic AI-generated speech, moving beyond simple emotion tagging to fine-grained acoustic attribute manipulation.
The ability to independently steer acoustic styles without changing the speaker identity in text-to-speech models addresses a core limitation, opening up new possibilities for content creation, accessibility, and human-computer interaction.
- · AI voiceover industry
- · Content creators
- · Assistive technology developers
- · Gaming industry
- · Monotonous legacy TTS systems
- · Low-quality voice synthesis services
More natural and customizable AI-generated voices will become prevalent across various applications.
This improved control will accelerate the adoption of AI voices in sensitive applications where precise tonal and emotional delivery is critical.
The blurring line between human and AI speech could lead to new challenges in authentication and misinformation, requiring advanced detection mechanisms.
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