Speech Recognition Model Improves Text-to-Speech Synthesis using Fine-Grained Reward

• URL: http://arxiv.org/abs/2511.17555v1
• Date: Wed, 12 Nov 2025 17:30:13 GMT
• Title: Speech Recognition Model Improves Text-to-Speech Synthesis using Fine-Grained Reward
• Authors: Guansu Wang, Peijie Sun,
• Abstract summary: We introduce Word-level TTS Alignment by ASR-driven Attentive Reward (W3AR)<n>W3AR uses attention from a pre-trained ASR model to drive finer-grained alignment and optimization of sequences predicted by a TTS model.<n> Experiments show that W3AR improves the quality of existing TTS systems and strengthens zero-shot robustness on unseen speakers.
• Score: 4.375679183191095
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in text-to-speech (TTS) have enabled models to clone arbitrary unseen speakers and synthesize high-quality, natural-sounding speech. However, evaluation methods lag behind: typical mean opinion score (MOS) estimators perform regression over entire utterances, while failures usually occur in a few problematic words. We observe that encoder-decoder ASR models (e.g., Whisper) surface word-level mismatches between speech and text via cross-attention, providing a fine-grained reward signal. Building on this, we introduce Word-level TTS Alignment by ASR-driven Attentive Reward (W3AR). Without explicit reward annotations, W3AR uses attention from a pre-trained ASR model to drive finer-grained alignment and optimization of sequences predicted by a TTS model. Experiments show that W3AR improves the quality of existing TTS systems and strengthens zero-shot robustness on unseen speakers. More broadly, our results suggest a simple recipe for generative modeling: understanding models can act as evaluators, delivering informative, fine-grained feedback for optimization.

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