ISCA Archive Interspeech 2021
ISCA Archive Interspeech 2021

Phrase Break Prediction with Bidirectional Encoder Representations in Japanese Text-to-Speech Synthesis

Kosuke Futamata, Byeongseon Park, Ryuichi Yamamoto, Kentaro Tachibana

We propose a novel phrase break prediction method that combines implicit features extracted from a pre-trained large language model, a.k.a BERT, and explicit features extracted from BiLSTM with linguistic features. In conventional BiLSTM-based methods, word representations and/or sentence representations are used as independent components. The proposed method takes account of both representations to extract the latent semantics, which cannot be captured by previous methods. The objective evaluation results show that the proposed method obtains an absolute improvement of 3.2 points for the F1 score compared with BiLSTM-based conventional methods using linguistic features. Moreover, the perceptual listening test results verify that a TTS system that applied our proposed method achieved a mean opinion score of 4.39 in prosody naturalness, which is highly competitive with the score of 4.37 for synthesized speech with ground-truth phrase breaks.


doi: 10.21437/Interspeech.2021-252

Cite as: Futamata, K., Park, B., Yamamoto, R., Tachibana, K. (2021) Phrase Break Prediction with Bidirectional Encoder Representations in Japanese Text-to-Speech Synthesis. Proc. Interspeech 2021, 3126-3130, doi: 10.21437/Interspeech.2021-252

@inproceedings{futamata21_interspeech,
  author={Kosuke Futamata and Byeongseon Park and Ryuichi Yamamoto and Kentaro Tachibana},
  title={{Phrase Break Prediction with Bidirectional Encoder Representations in Japanese Text-to-Speech Synthesis}},
  year=2021,
  booktitle={Proc. Interspeech 2021},
  pages={3126--3130},
  doi={10.21437/Interspeech.2021-252}
}