"A Voice Conversion Algorithm Based on Gaussian Mixture Model
       with Dynamic Frequency Warping"
Dr Tomoki Toda

Abstract: 
   Speech of various speakers can be synthesized by utilizing a
   voice conversion technique that can control speaker
   individuality. The voice conversion algorithm based on the Gaussian
   Mixture Model (GMM), which is a conventional statistical voice
   conversion algorithm proposed by Stylianou et al., can convert
   speech features continuously by using the correlation between a
   source feature and a target feature. However, the quality of the
   converted speech is degraded because the converted spectrum is
   excessively smoothed by the statistical averaging operation. In
   this talk, we propose a GMM-based algorithm with Dynamic Frequency
   Warping (DFW) to avoid such over-smoothing. In the proposed
   algorithm, the converted spectrum is calculated by mixing the
   GMM-based converted spectrum and the DFW-based converted spectrum
   to avoid deterioration of spectral conversion-accuracy. Results of
   evaluation experiments clarify that the proposed algorithm can
   improve the quality of the converted speech while maintaining the
   same conversion-accuracy for speaker individuality as the GMM-based
   algorithm.

Speaker Background: 
Dr Toda is currently visiting CMU for a year as a
postdoc working on voice transformation with Alan W Black.  He 
did his thesis at Nara Institute of Science and Technology and
has most recently been working at ATR, and Nagoya Institute of 
Technology with Prof Keiichi Tokuda.