An Evaluation of Parameter Generation Methods with Rich Context Models in HMM-Based Speech Synthesis

In this paper, we propose parameter generation methods using rich context models in HMM-based speech synthesis as yet another hybrid method combining HMM-based speech synthesis and unit selection synthesis. In the traditional HMM-based speech synthesis, generated speech parameters tend to be excessively smoothed and they cause muffled sounds in synthetic speech. To alleviate this problem, several hybrid methods have been proposed. Although they significantly improve quality of synthetic speech by directly using natural waveform segments, they usually lose flexibility in converting synthetic voice characteristics. In the proposed methods, rich context models representing individual acoustic parameter segments are reformed as GMMs and a speech parameter sequence is generated from them using the parameter generation algorithm based on the maximum likelihood criterion. Since a basic framework of the proposed methods is still the same as the traditional framework, the capability of flexibly modeling acoustic features remains. We conduct several experimental evaluations of the proposed methods from various perspectives. The experimental results demonstrate that the proposed methods yield significant improves in quality of synthetic speech.