A Speaker-Adaptive HMM-based Vietnamese Text-to-Speech System

This paper describes the first attempt in developing a Vietnamese HMM-based Text-to-Speech system using the speaker-adaptive approach. Although speaker-dependent systems have been built widely, no speaker-adaptive system has been developed for Vietnamese so far. We collected speech data from several Vietnamese native speakers and employed state-of-the-art speech analysis, model training and speaker adaptation techniques to develop the system. Besides, we performed perceptual experiments to compare the quality of speaker-adapted (SA) voices built on the average voice model and speaker-dependent (SD) voices built on SD models, and to confirm the effects of contextual features including word boundary (WB) and part-of-speech (POS) on the quality of synthetic speech. Evaluation results show that SA voices have significantly higher naturalness than SD voices when the same limited contextual feature set excluding WB and POS was used. In addition, SA voices trained with limited contextual features excluding WB and POS still have better quality than SD voices trained with full contextual features including WB and POS. These results show the robustness of the speaker-adaptive over the speaker-dependent approach for Vietnamese statistical parametric speech synthesis.