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Phonetic Enhanced Language Modeling for Text-to-Speech Synthesis
- Publication Year :
- 2024
-
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.<br />Comment: Accepted by Interspeech 2024
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2406.02009
- Document Type :
- Working Paper