Local minimum generation error criterion for hybrid HMM speech synthesis

Abstract

This paper presents an HMM-driven hybrid speech synthesis approach in which unit selection concatenative synthesis is used to improve the quality of the statistical system using a Local Minimum Generation Error (LMGE) during the synthesis stage. The idea behind this approach is to combine the robustness due to HMMs with the naturalness of concatenated units. Unlike the conventional hybrid approaches to speech synthesis that use concatenative synthesis as a backbone, the proposed system employs stable regions of natural units to improve the statistically generated parameters. We show that this approach improves the generation of vocal tract parameters, smoothes the bad joints and increases the overall quality.