Realization of average-voice-based speech synthesis with diverse voices and speaking styles

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The purpose of this research is the realization of text-to-speech synthesis that can generate speech with an arbitrarily given speaker's voice and diverse speaking styles and/or emotional expressions. We have obtained the following results.1. Speech synthesis with arbitrary speaker's voice based on average voice modelWe have proposed a new training method of average voice model for speech synthesis in which an arbitrary speaker's voice is generated based on speaker adaptation. We have also proposed new speaker adaptation techniques based on hidden semi-Markov model (HSMM) that can model phone duration more precisely than the conventional hidden Markov model (HMM). From the results of objective and subjective evaluation tests, it has been shown that the average-voice-model-based speech synthesis can generates natural sounding speech of the target speaker.2. Speech synthesis with various speaking styles and emotional expressionsWe have proposed several approaches to the realization of emotional expressivity and speaking style variability in text-to-speech synthesis. We investigated two methods for modeling speaking styles and/or emotional expressions based on an HMM-based speech synthesis framework, and then proposed some approaches to adding various styles to synthetic speech, such as style interpolation, style morphing, style adaptation, and style control techniques. From results of subjective experiments, we have shown that the effectiveness of the proposed approaches.3. ProsodyWe have developed a robust fundamental frequency estimation and voice/unvoiced determination technique based on instantaneous frequency amplitude spectrum. We have also proposed modeling techniques for phone duration and pause for high quality text-to-speech synthesis.