Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/102726
Title: | Mixture of factor analyzers using priors from non-parallel speech for voice conversion | Authors: | Wu, Zhizheng Kinnunen, Tomi Chng, Eng Siong Li, Haizhou |
Keywords: | DRNTU::Engineering::Computer science and engineering | Issue Date: | 2012 | Source: | Wu, Z., Kinnunen, T., Chng, E. S., & Li, H. (2012). Mixture of factor analyzers using priors from non-parallel speech for voice conversion. IEEE signal processing letters, 19(12), 914-917. | Series/Report no.: | IEEE signal processing letters | Abstract: | A robust voice conversion function relies on a large amount of parallel training data, which is difficult to collect in practice. To tackle the sparse parallel training data problem in voice conversion, this paper describes a mixture of factor analyzers method which integrates prior knowledge from non-parallel speech into the training of conversion function. The experiments on CMU ARCTIC corpus show that the proposed method improves the quality and similarity of converted speech. With both objective and subjective evaluations, we show the proposed method outperforms the baseline GMM method. | URI: | https://hdl.handle.net/10356/102726 http://hdl.handle.net/10220/16436 |
ISSN: | 1070-9908 | DOI: | 10.1109/LSP.2012.2225615 | Schools: | School of Computer Engineering | Research Centres: | Temasek Laboratories | Rights: | © 2012 IEEE | Fulltext Permission: | none | Fulltext Availability: | No Fulltext |
Appears in Collections: | SCSE Journal Articles |
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