An F0 modeling technique based on prosodic events for spontaneous speech synthesis
Tomoki Koriyama, Takashi Nose, Takao Kobayashi
Abstract
Open-access reader
Tomoki Koriyama, Takashi Nose, Takao Kobayashi
Abstract
Open-access reader
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.
OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
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)