Comprehensive voice conversion analysis based on DGMM and feature combination

Voice conversion system modifies a speaker's voice to be perceived as another speaker uttered, and now it is widely used in many real applications. However, most research only focuses on one aspect performance of voice conversion system, rare theoretical analysis and experimental comparison on the whole source-target speaker voice conversion process has been introduced. Therefore, in this paper, a comprehensive analysis on source-target speaker voice conversion is conducted based on three key steps, including acoustic features selection and extraction, voice conversion model construction, and target speech synthesis, and a complete and optimal source-target speaker voice conversion is proposed. First, a comprehensive feature combination form consisting of prosodic feature, spectrum parameter and spectral envelope characteristic, is proposed. Then, to void the discontinuity and spectrum distortion of a converted speech, DGMM (Dynamic Gaussian Mixture Model) considering dynamic information between frames is presented. Subsequently, for speech synthesis, STRAIGHT algorithm synthesizer with feature combination is modified. Finally, the objective contrast experiment shows that our new source-target voice conversion process achieves better performance than the conventional methods. In addition, the speaker recognition system is also used to evaluate the quality of converted speech, and experimental result shows that the converted speech has higher target speaker individuality and speech quality.