標題: A Study of Mutual Information for GMM-Based Spectral Conversion
作者: Hwang, Hsin-Te
Tsao, Yu
Wang, Hsin-Min
Wang, Yih-Ru
Chen, Sin-Horng
電機工程學系
Department of Electrical and Computer Engineering
關鍵字: Voice conversion;mutual information;GMM
公開日期: 2012
摘要: The Gaussian mixture model (GMM)-based method has dominated the field of voice conversion (VC) for last decade. However, the converted spectra are excessively smoothed and thus produce muffled converted sound. In this study, we improve the speech quality by enhancing the dependency between the source (natural sound) and converted feature vectors (converted sound). It is believed that enhancing this dependency can make the converted sound closer to the natural sound. To this end, we propose an integrated maximum a posteriori and mutual information (MAPMI) criterion for parameter generation on spectral conversion. Experimental results demonstrate that the quality of converted speech by the proposed MAPMI method outperforms that by the conventional method in terms of formal listening test.
URI: http://hdl.handle.net/11536/22051
ISBN: 978-1-62276-759-5
期刊: 13TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2012 (INTERSPEECH 2012), VOLS 1-3
起始頁: 78
結束頁: 81
顯示於類別:會議論文