VoiceTTA: Enhancing Zero-Shot Text-to-Speech via Reinforcement Learning-Based Test-Time Adaptation
arXiv:2606.26534v1 Announce Type: cross Abstract: Recently, zero-shot text-to-speech (TTS) has enabled high-fidelity and expressive speech synthesis, but it often fails to imitate unseen speaking styles from uncommon scenarios (e.g., crosstalk, dialects). Moreover, fine-tuning pretrained models requires large, high-quality datasets, limiting rapid personalization. We propose VoiceTTA, a reinforcement learning-based test-time adaptation (TTA) method that improves voice imitation of pretrained zero-shot TTS models. VoiceTTA introduces two style rewards based on coefficient-of-variation differenc
The proliferation of zero-shot TTS models has highlighted limitations in adapting to uncommon speech styles, creating a need for more robust, on-the-fly personalization methods without extensive retraining.
This breakthrough improves the versatility and fidelity of synthetic speech, making AI-generated voices more natural and adaptable to diverse and nuanced scenarios, which is crucial for advanced AI applications.
Zero-shot TTS models can now dynamically adapt to new speaking styles with higher accuracy and less data, enabling more rapid and personalized speech synthesis without the need for large, high-quality fine-tuning datasets.
- · AI developers
- · Content creators
- · Accessibility tech
- · Custom voice agents
- · Traditional TTS fine-tuning methods
- · Generative AI models with poor adaptation
The quality and realism of AI-generated speech improve significantly for niche and uncommon speaking styles.
This could accelerate the deployment of highly personalized voice assistants and conversational AI across various applications.
Increased hyper-realistic deepfakes of voices with unique regional accents or tones could emerge, raising concerns about authenticity and misuse.
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Read at arXiv cs.AI