Minimum generation error criterion considering global/local variance for HMM-based speech synthesis
Yi-Jian Wu, Heiga Zen, Yoshihiko Nankaku, Keiichi Tokuda · IEEE International Conference on Acoustics Speech and Signal Processing · 2008
Two techniques, including minimum generation error (MGE) criterion for HMM training, and the parameter generation algorithm considering global variance (GV), had been proposed to improve the quality of HMM-based speech synthesis. In this paper, we incorporate the GV technique into MGE criterion, where an additional generation error component considering global/local variance (GV/LV) is introduced for generation error definition, and the model parameters are optimized to minimize the new generation error function. From the experimental results, the quality of synthesized speech was improved after MGE-GV/LV training, which is similar to the effectiveness of considering GV in parameter generation, however, without introducing any extra computational cost in synthesis process.