RobustSpeechFlow: Learning Robust Text-to-Speech Trajectories via Augmentation-based Contrastive Flow Matching
arXiv:2605.22083v1 Announce Type: cross Abstract: While flow-matching text-to-speech (TTS) achieves strong zero-shot speaker similarity and naturalness, it remains susceptible to content fidelity issues, particularly skip and repeat errors from imperfect alignment. We propose RobustSpeechFlow, a training strategy that improves alignment robustness by extending contrastive flow matching with length-preserving repeat and skip latent augmentations. Requiring no external aligners or preference data, our method directly penalizes realistic failure modes and readily integrates into existing pipeline
The continuous drive for more robust and natural AI-generated speech, particularly in flow-matching models, necessitates ongoing research into addressing persistent fidelity issues like skip and repeat errors.
Improving the reliability and naturalness of text-to-speech models helps consolidate AI's role in various applications, enhancing user experience and reducing the need for costly manual interventions.
Flow-matching TTS models can now achieve stronger zero-shot speaker similarity and naturalness with significantly fewer content fidelity issues, reducing failure rates in high-stakes conversational AI and content generation.
- · AI-powered content creation platforms
- · Customer service and conversational AI companies
- · Speech synthesis developers
- · Accessibility technology providers
- · Manual voice-over artists (for certain applications)
- · Companies relying on less robust legacy TTS systems
Widespread adoption of higher-fidelity text-to-speech in commercial applications.
Increased consumer expectation for natural and error-free AI interactions, pushing less advanced models out of the market.
The acceleration of personalized synthetic media creation, blurring lines between real and AI-generated content.
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Read at arXiv cs.LG