German and Czech Speech Synthesis Using HMM-Based Speech Segment Database

This paper presents an experimental German speech synthesis system. As in case of a Czech text-to-speech system ARTIC, statistical approach (using hidden Markov models) was employed to build a speech segment database. This approach was confirmed to be language independent and it was shown to be capable of designing a quality database that led to an intelligible synthetic speech of a high quality. Some experiments with clustering the similar speech contexts were performed to enhance the quality of the synthetic speech. Our results show the superiority of phoneme-level clustering to subphoneme-level one.