https://scholars.lib.ntu.edu.tw/handle/123456789/580913
標題: | Semi-supervised learning for multi-speaker text-to-speech synthesis using discrete speech representation | 作者: | Tu T Chen Y.-J Liu A.H HUNG-YI LEE |
關鍵字: | Speech communication; Speech synthesis; Data collection process; Encoder-decoder; High quality; Semi-supervised; Speaker characteristics; Speech data; Text-to-speech system; TTS systems; Semi-supervised learning | 公開日期: | 2020 | 卷: | 2020-October | 起(迄)頁: | 3191-3195 | 來源出版物: | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH | 摘要: | Recently, end-to-end multi-speaker text-to-speech (TTS) systems gain success in the situation where a lot of high-quality speech plus their corresponding transcriptions are available. However, laborious paired data collection processes prevent many institutes from building multi-speaker TTS systems of great performance. In this work, we propose a semi-supervised learning approach for multi-speaker TTS. A multi-speaker TTS model can learn from the untranscribed audio via the proposed encoder-decoder framework with discrete speech representation. The experiment results demonstrate that with only an hour of paired speech data, whether the paired data is from multiple speakers or a single speaker, the proposed model can generate intelligible speech in different voices. We found the model can benefit from the proposed semi-supervised learning approach even when part of the unpaired speech data is noisy. In addition, our analysis reveals that different speaker characteristics of the paired data have an impact on the effectiveness of semi-supervised TTS. Copyright ? 2020 ISCA |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098152714&doi=10.21437%2fInterspeech.2020-1824&partnerID=40&md5=48472c4b0d8ae99398db9d9cdb29e3fe https://scholars.lib.ntu.edu.tw/handle/123456789/580913 |
ISSN: | 2308457X | DOI: | 10.21437/Interspeech.2020-1824 |
顯示於: | 電機工程學系 |
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