arXiv:2606.20518v1 Announce Type: new Abstract: Flow-matching text-to-speech systems achieve remarkable zero-shot quality but remain static after deployment: pronunciation errors on out-of-vocabulary proper nouns persist unless the model is retrained. We introduce FlowEdit, a life-long adaptation framework for frozen flow-matching TTS that learns pronunciation corrections as latent conditioning edits rather than weight updates. When corrective feedback is provided, FlowEdit optimizes a token-level perturbation in the text embedding space, then stores the correction in a Modern Hopfield Network
The proliferation of advanced TTS systems highlights the persistent 'cold start' problem of adapting them to new, out-of-vocabulary data such as proper nouns, driving the need for more efficient adaptation mechanisms.
This development allows for continuous, efficient adaptation of speech synthesis models post-deployment, addressing a critical bottleneck in personalized and dynamic AI applications without costly full retraining.
TTS systems can now correct pronunciation errors on the fly using latent conditioning edits and associative memory, transitioning from static, retraining-dependent models to dynamically adaptable ones.
- · AI developers
- · Customer service platforms
- · Content creators
- · Voice AI industry
- · Traditional TTS retraining methods
Improved user experience for AI-generated speech, especially in niche or personalized applications.
Reduced operational costs and faster deployment of customized voice AI solutions across industries.
Accelerated development of highly personalized and adaptive AI agents capable of continuous self-improvement in natural language interaction.
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Read at arXiv cs.AI