| Autor: |
Yasuda, Yusuke, Toda, Tomoki |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
IEEE Journal of Selected Topics in Signal Processing (Volume: 16, Issue: 6, October 2022) |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/JSTSP.2022.3190672 |
| Popis: |
End-to-end text-to-speech synthesis (TTS) can generate highly natural synthetic speech from raw text. However, rendering the correct pitch accents is still a challenging problem for end-to-end TTS. To tackle the challenge of rendering correct pitch accent in Japanese end-to-end TTS, we adopt PnG~BERT, a self-supervised pretrained model in the character and phoneme domain for TTS. We investigate the effects of features captured by PnG~BERT on Japanese TTS by modifying the fine-tuning condition to determine the conditions helpful inferring pitch accents. We manipulate content of PnG~BERT features from being text-oriented to speech-oriented by changing the number of fine-tuned layers during TTS. In addition, we teach PnG~BERT pitch accent information by fine-tuning with tone prediction as an additional downstream task. Our experimental results show that the features of PnG~BERT captured by pretraining contain information helpful inferring pitch accent, and PnG~BERT outperforms baseline Tacotron on accent correctness in a listening test. |
| Databáze: |
arXiv |
| Externí odkaz: |
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