This paper presents a novel method for training hidden Markov models (HMMs) for use in HMM-based speech synthesis. The primary goal of HMM parameter optimization is to ensure that parameters generated from the trained models exhibit similar properties to natural speech. In this paper, two major problems in conventional training are addressed: 1) the inconsistency between the training and synthesis optimization criterion; and 2) the over-smoothing caused by the statistical modeling process. The proposed method integrates the global variance (GV) criterion into a trajectory training method to give a unified framework for both training and synthesis which provides both a consistent optimization criterion and a closed form solution for parameter generation. The experimental results demonstrate that the proposed method yields a significant improvement in the naturalness of synthetic speech.
['Tomoki Toda', 'Steve J. Young']
Trajectory training considering global variance for HMM-based speech synthesis