GRAFT: Grafted Reference Audio for Fine-grained Pronunciation in Zero-shot Text-to-Speech
GRAFT is a per-word pronunciation conditioning mechanism for text-to-speech neural codec language modeling. It controls pronunciation of a chosen word from a short spoken sample, using voice conversion to decouple hint speaker from target speaker. In a blind English listening study, human raters rank GRAFT first, and on a five-language benchmark, it reduced target-word phoneme error rate by 22-39% while preserving speaker similarity and naturalness.
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[Submitted on 2 Jul 2026]
Title:GRAFT: Grafted Reference Audio for Fine-grained Pronunciation in Zero-shot Text-to-Speech
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Abstract:We present GRAFT, a per-word pronunciation conditioning mechanism for text-to-speech neural codec language modeling. Existing systems reach high intelligibility and naturalness but inherit the ambiguity of text and mispronounce rare proper nouns, loanwords and technical terms. Even phoneme-conditioned models offer no direct acoustic handle for per-word pronunciation. GRAFT controls the pronunciation of a chosen word from a short spoken sample of it, encoded with the model's own speech tokenizer and bound to the word's position in the prompt. Voice conversion during training-data construction disentangles the hint speaker from the target speaker, so the hint may come from any voice while the output stays in the target voice. In a blind English listening study, human raters rank GRAFT first by a clear margin, judging its rendering of the difficult word closest to a reference recording of that word. On a five-language objective benchmark, GRAFT reduces target-word phoneme error rate by 22-39% over the identical text-only backbone and outperforms competitive open-source zero-shot systems, both phoneme- and text-conditioned, on target-word pronunciation, while preserving speaker similarity and naturalness.
Subjects:
Machine Learning (cs.LG); Computation and Language (cs.CL)
Cite as: arXiv:2607.02633 [cs.LG]
(or arXiv:2607.02633v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.02633
arXiv-issued DOI via DataCite
Submission history
From: Antonis Asonitis [view email] [v1] Thu, 2 Jul 2026 14:40:21 UTC (367 KB)
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