Full metadata record
DC FieldValueLanguage
dc.contributor.authorHwang, Hsin-Teen_US
dc.contributor.authorTsao, Yuen_US
dc.contributor.authorWang, Hsin-Minen_US
dc.contributor.authorWang, Yih-Ruen_US
dc.contributor.authorChen, Sin-Horngen_US
dc.date.accessioned2017-04-21T06:49:29Z-
dc.date.available2017-04-21T06:49:29Z-
dc.date.issued2015en_US
dc.identifier.isbn978-988-14768-0-7en_US
dc.identifier.urihttp://hdl.handle.net/11536/136226-
dc.description.abstractVoice conversion (VC) using artificial neural networks (ANNs) has shown its capability to produce better sound quality of the converted speech than that using Gaussian mixture model (GMM). Although ANN-based VC works reasonably well, there is still room for further improvement. One of the promising ways is to adopt the successful techniques in statistical model-based parameter generation (SMPG), such as trajectory-based mapping approaches that are originally designed for GMM-based VC and hidden Markov model (HMM)-based speech synthesis. This study presents a probabilistic interpretation for ANN-based VC. In this way, ANN-based VC can easily incorporate the successful techniques in SMPG. Experimental results demonstrate that the performance of ANN-based VC can be effectively improved by two trajectory-based mapping techniques (maximum likelihood parameter generation (MLPG) algorithm and maximum likelihood-based trajectory mapping considering global variance (referred to as MLGV)), compared to the conventional ANN-based VC with frame-based mapping and the GMM-based VC with the MLPG algorithm. Moreover, ANN-based VC with the trajectory-based mapping techniques can achieve comparable performance when compared to the state-of-the-art GMM-based VC with the MLGV algorithm.en_US
dc.language.isoen_USen_US
dc.titleA Probabilistic Interpretation for Artificial Neural Network-based Voice Conversionen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2015 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA)en_US
dc.citation.spage552en_US
dc.citation.epage558en_US
dc.contributor.department電機學院zh_TW
dc.contributor.departmentCollege of Electrical and Computer Engineeringen_US
dc.identifier.wosnumberWOS:000382954100106en_US
dc.citation.woscount0en_US
Appears in Collections:Conferences Paper