Using Hybrid HMM-Based Speech Segmentation to Improve Synthetic Speech Quality

The automatic phonetic time-alignment of speech databases is essential for the development cycle of a text-to-speech (TTS) system. Furthermore, the quality of the synthesized speech signals is strongly related to the precision of the produced alignment. In the present work we study the performance of a new HMM-based speech segmentation method. The method is based on hybrid embedded and isolated-unit trained models, and has proved to improve the phonetic segmentation accuracy in the multiple speaker task. Here it is employed on the single speaker segmentation task, utilizing a Greek-speech database. The evaluation of the method showed significant improvement in terms of phonetic segmentation accuracy as well as in the perceptual quality of synthetic speech, when compared to the baseline system.