2012•Unpublished venueOpen access

An F0 modeling technique based on prosodic events for spontaneous speech synthesis

Tomoki Koriyama, Takashi Nose, Takao Kobayashi

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Abstract

This paper proposes a technique for effective modeling of F0 contours using prosodic-event-based HMM units for HMM-based spontaneous speech synthesis. The modeling unit corresponds to one of prosodic event segments such as pitch falling by accent and pitch rising by boundary pitch movement (BPM). Since the prosodic events of one phrase are generally less frequent than the changes of phonemes, the proposed unit is expected to reduce the number of model parameters of F0, which leads to robust parameter estimation. The objective and subjective experiments using spontaneous conversational speech data show that the proposed technique can significantly reduce the number of model parameters while keeping the naturalness of the synthetic speech.

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What this paper is about

This paper proposes a technique for effective modeling of F0 contours using prosodic-event-based HMM units for HMM-based spontaneous speech synthesis. The modeling unit corresponds to one of prosodic event segments such as pitch falling by accent and pitch rising by boundary pitch movement (BPM). Since the prosodic events of one phrase are generally less frequent than the changes of phonemes, the proposed unit is expected to reduce the number of model parameters of F0, which leads to robust parameter estimation. The objective and subjective experiments using spontaneous conversational speech data show that the proposed technique can significantly reduce the number of model parameters while keeping the naturalness of the synthetic speech.

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Available abstract

This paper proposes a technique for effective modeling of F0 contours using prosodic-event-based HMM units for HMM-based spontaneous speech synthesis. The modeling unit corresponds to one of prosodic event segments such as pitch falling by accent and pitch rising by boundary pitch movement (BPM). Since the prosodic events of one phrase are generally less frequent than the changes of phonemes, the proposed unit is expected to reduce the number of model parameters of F0, which leads to robust parameter estimation. The objective and subjective experiments using spontaneous conversational speech data show that the proposed technique can significantly reduce the number of model parameters while keeping the naturalness of the synthetic speech.

Key concepts: Naturalness, Hidden Markov model, Speech synthesis, Speech recognition, Pitch accent, Computer science, Phrase, Event (particle physics)

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