VoiceTTA enhances zero-shot TTS with reinforcement learning

Researchers have developed VoiceTTA, a novel method that enhances zero-shot text-to-speech (TTS) models using reinforcement learning for test-time adaptation. This approach aims to improve the imitation of unseen speaking styles and uncommon scenarios, such as crosstalk or dialects, without requiring extensive fine-tuning datasets. VoiceTTA incorporates style rewards based on F0 and energy variations, alongside speaker similarity and intelligibility metrics derived from a Whisper model, optimizing learnable prefixes during inference. AI

IMPACT This research could lead to more adaptable and personalized speech synthesis models, improving user experience in various applications.

RANK_REASON The cluster contains a research paper detailing a new method for text-to-speech synthesis. [lever_c_demoted from research: ic=1 ai=1.0]

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• VoiceTTA
• Whisper

• paper
• model release

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