Abstract A Minimum Generation Error (MGE) training method based on perceptually weighted Line Spectral Pair (LSP) distance is proposed to improve the performance of Hidden Markov Model (HMM) based parametric speech synthesis system. The generation error defined by Euclidean distance used in the traditional MGE training, is not eligible in measuring the real gap between generated spectrum and natural spectrum when the speech spectrum is described by LSP. Although using generation error defined by Log Spectral Distortion (LSD) having nothing to do with spectrum parameters manages to deal with this problem, the improvement seems trivial compared to the incurred higher computational complexity. In this paper, an MGE training criterion based on weighted LSP distance is proposed, and this MGE training method is subjectively and objectively contrasted with different weighted methods and LSD based MGE training method. Eventually, a perceptually weighted training method is obtained, which not only achieves the best performance, but also incurs no extra computational complexity compared with the traditional MGE training.
LEI Ming,LING Zhen-Hua,DAI Li-Rong. Minimum Generation Error Training Based on Perceptually Weighted Line Spectral Pair Distance for Statistical Parametric Speech Synthesis[J]. , 2010, 23(4): 572-579.
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2010, Vol. 23 
