Yusuke Ijima

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This paper proposes a technique for emotional speech recognition which enables us to extract paralinguistic information as well as linguistic information contained in speech signal. The technique is based on style estimation and style adaptation using multiple-regression HMM. Recognition process consists of two stages. In the first stage, a style vector(More)
This paper proposes an unsupervised labeling technique using phrase-level prosodic contexts for HMM-based expressive speech synthesis, which enables users to manually enhance prosodic variations of synthetic speech without degrading the naturalness. In the proposed technique, HMMs are first trained using the conventional labels including only linguistic(More)
This paper proposes a technique for adding more prosodic variations to the synthetic speech in HMM-based expressive speech synthesis. We create novel phrase-level F0 context labels from the residual information of F0 features between original and synthetic speech for the training data. Specifically, we classify the difference of average log F0 values(More)