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FlowEdit: Associative Memory for Lifelong Pronunciation Adaptation in Flow-Matching TTS
[Submitted on 18 Jun 2026] · 2026-06-19 · via cs.AI updates on arXiv.org

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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 serving as content-addressable episodic memory. At inference, corrections are retrieved via soft attention with a similarity gate, enabling fuzzy morphological matching. On our curated benchmark of 312 multilingual proper nouns across 18 language families, FlowEdit reduces target-word Phoneme Error Rate by 92.7% relative to the zero-shot baseline while maintaining identical general-speech quality. Corrections complete in approximately 15 seconds on a single GPU.

Submission history

From: Nityanand Mathur Mr [view email]
[v1] Thu, 18 Jun 2026 17:36:02 UTC (828 KB)

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