Towards the Development of Speaker-Dependent and Speaker-Independent Hidden Markov Model-Based Thai Speech Synthesis

Abstract

Problem statement: Tone distortion in Thai languages can deteriorate not only the intelligibility of speech but also its naturalness. Therefore, the correctness of tone must be carefully taken into account in continuous speech synthesis. The preliminary work confronted this problem when applying HMM-based speech synthesis to Thai. Approach: This study presented a study on speaker-dependent and speaker-independent Hidden Markov Model (HMM)-based Thai speech synthesis. In the speaker-dependent system, we developed a simple tone-separated tree structure in the tree-based context clustering process of the training stage to treat the tone distortion problem. In the speaker-independent system or averaged-voice-model system, a number of tonal features are extracted and applied with the Speaker Adaptive Training (SAT) and Shared Decision Tree (STC) techniques to release the tone distortion problem. Results: Our objective evaluation revealed that the proposed features could make the F0 contour closer to the target speaker’s real contour. The results from our subjective test also revealed that the proposed tonal features could improve the tone intelligibility of all speech-model scenarios of male and female. Conclusion: By applying our approach, the problem of tone distortion can be relieved effectively. The better tone correctness can improve the intelligibility and the naturalness of speech significantly.