Voice conversion using speaker-dependent conditional restricted Boltzmann machine-Reference-Cited by-同舟云学术

Voice conversion using speaker-dependent conditional restricted Boltzmann machine

Published:2015-02-25 Issue:1 Volume:2015 Page:
ISSN:1687-4722
Container-title:EURASIP Journal on Audio, Speech, and Music Processing
language:en
Short-container-title:J AUDIO SPEECH MUSIC PROC.

Author:

Nakashika Toru,Takiguchi Tetsuya,Ariki Yasuo

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Acoustics and Ultrasonics

Link

http://link.springer.com/article/10.1186/s13636-014-0044-3/fulltext.html

Reference44 articles.

1. A Kain, MW Macon, in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Spectral voice conversion for text-to-speech synthesis, (1998), pp. 285–288.

2. C Veaux, X Robet, in Proceedings of Interspeech. Intonation conversion from neutral to expressive speech, (2011), pp. 2765–2768.

3. K Nakamura, T Toda, H Saruwatari, K Shikano, Speaking-aid systems using GMM-based voice conversion for electrolaryngeal speech. Speech Commun. 54(1), 134–146 (2012).

4. L Deng, A Acero, L Jiang, J Droppo, X Huang, in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). High-performance robust speech recognition using stereo training data, (2001), pp. 301–304.

5. A Kunikoshi, Y Qiao, N Minematsu, K Hirose, in Proceedings of Interspeech. Speech generation from hand gestures based on space mapping, (2009), pp. 308–311.

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