[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5916/jkosme.2014.38.2.182

A nonlinear transformation methods for GMM to improve over-smoothing effect

Chae, Yi Geun (Department of Computer Engineering, College of Engineering, Kongju National University)

Publication Information

Journal of Advanced Marine Engineering and Technology / v.38, no.2, 2014 , pp. 182-187 More about this Journal

Abstract

We propose nonlinear GMM-based transformation functions in an attempt to deal with the over-smoothing effects of linear transformation for voice processing. The proposed methods adopt RBF networks as a local transformation function to overcome the drawbacks of global nonlinear transformation functions. In order to obtain high-quality modifications of speech signals, our voice conversion is implemented using the Harmonic plus Noise Model analysis/synthesis framework. Experimental results are reported on the English corpus, MOCHA-TIMIT.

Keywords

nonlinear transformation; GMM Method; RBF; Over-smoothing; Piecewise RBF;

Citations & Related Records

Reference

1	S. Moon, "Enhancement of ship's wheel order recognition system using speaker's intention predictive parameters", Journal of Society of Marine Engineering, vol. 32, no. 5, pp. 791-797, 2008.
2	S. Nakamura, K. Shikano, and H. Kuwabara, "Voice conversion through vector quantization," Proceedings of Internaltional Conference on Acoustics, Speech and Signal Processing, pp. 655-658, 1988.
3	Y. Stylianou, O. Cappe, and E. Moulines, "Continuous probabilistic transform for voice conversion," IEEE Transactions on Speech and Audio Processing, vol. 6, no. 2, 1998.
4	A. Kain and M. Macon, "Spectral voice conversion for text-to-speech synthesis," Proceedings of Internaltional Conference on Acoustics, Speech and Signal Processing, pp. 285-288, 1988.
5	A. Kain, "High resolution voice transformation," Ph.D dissertation, Oregon Graduate Institute of Science and Technology, 2001.
6	A. Dempster, N. Laird, and D. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," Journal of Royal Statistical Society, vol. 39, pp. 1-22 and 22-38, 1977.
7	C. Orphanidou, I. Moroz, and S. Roberts, "Wavelet-based Voice morphing", Journal of World Scientific and Engineering Academy and Society, vol. 10, no. 3, pp. 3297-3302, 2004.
8	G. Baudoin and Y. Stylianou, "On the transformation of speech spectrum for voice conversion," Proceedings of International Conference on Spoken Language Processing, pp. 1405-1408, 1996.
9	C. Bishop, "Neural networks for pattern recognition," Clarendon Press, Oxford, 1995.