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arXiv · 2406.02009

Phonetic Enhanced Language Modeling for Text-to-Speech Synthesis

Kun Zhou·Shengkui Zhao·Yukun Ma·Chong Zhang·Hao Wang·Dianwen Ng·Chongjia Ni·Nguyen Trung Hieu·Jia Qi Yip·Bin Ma
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Abstract

Recent language model-based text-to-speech (TTS) frameworks demonstrate scalability and in-context learning capabilities. However, they suffer from robustness issues due to the accumulation of errors in speech unit predictions during autoregressive language modeling. In this paper, we propose a phonetic enhanced language modeling method to improve the performance of TTS models. We leverage self-supervised representations that are phonetically rich as the training target for the autoregressive language model. Subsequently, a non-autoregressive model is employed to predict discrete acoustic codecs that contain fine-grained acoustic details. The TTS model focuses solely on linguistic modeling during autoregressive training, thereby reducing the error propagation that occurs in non-autoregressive training. Both objective and subjective evaluations validate the effectiveness of our proposed method.

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Kun Zhou, Shengkui Zhao, Yukun Ma, Chong Zhang, Hao Wang, Dianwen Ng, Chongjia Ni, Nguyen Trung Hieu, Jia Qi Yip, Bin Ma. 2024-06-04. Phonetic Enhanced Language Modeling for Text-to-Speech Synthesis. https://arxiv.org/abs/2406.02009

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