MAP-based adaptation for speech conversion using adaptation data selection and non-parallel training

Chung Han Lee, Chung Hsien Wu

研究成果: Conference contribution

79 引文 斯高帕斯(Scopus)

摘要

This study presents an approach to GMM-based speech conversion using maximum a posteriori probability (MAP) adaptation. First, a conversion function is trained using a parallel corpus containing the same utterances spoken by both the source and the reference speakers. Then a non-parallel corpus from a new target speaker is used for the adaptation of the conversion function which models the voice conversion between the source speaker and the new target speaker. The consistency among the adaptation data is estimated to select suitable data from the nonparallel corpus for MAP-based adaptation of the GMMs. In speech conversion evaluation, experimental results show that MAP adaptation using a small non-parallel corpus can reduce the conversion error and improve the speech quality for speaker identification compared to the method without adaptation. Objective and subjective tests also confirm the promising performance of the proposed approach.

原文English
主出版物標題INTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP
發行者International Speech Communication Association
頁面2254-2257
頁數4
ISBN(列印)9781604234497
出版狀態Published - 2006
事件INTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP - Pittsburgh, PA, United States
持續時間: 2006 九月 172006 九月 21

出版系列

名字INTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP
5

Other

OtherINTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP
國家/地區United States
城市Pittsburgh, PA
期間06-09-1706-09-21

All Science Journal Classification (ASJC) codes

  • 電腦科學(全部)

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