ML Parameter Generation with a Reformulated MGE Training Criterion — Participation in the Voice Conversion Challenge 2016

This paper describes our entry to the Voice Conversion Challenge 2016. Based on the maximum likelihood parameter generation algorithm, the method is a reformulation of the minimum generation error training criterion. It uses a GMM for soft classification, a Mel-cepstral vocoder for acoustic analysis and an improved dynamic time warping procedure for source-target alignment. To compensate the oversmoothing effect, the generated parameters are filtered through a speaker-independent postfilter implemented as a linear transform in cepstral domain. The process is completed with mean and variance adaptation of the log- fundamental frequency and duration modification by a constant factor. The results of the evaluation show that the proposed system achieves a high conversion accuracy in comparison with other systems, while its naturalness scores are intermediate.