Sayaka Shiota

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This paper provides an overview of speaker adaptation research carried out in the EMIME speech-to-speech translation (S2ST) project. We focus on how speaker adaptation transforms can be learned from speech in one language and applied to the acoustic models of another language. The adaptation is transferred across languages and/or from recognition models to(More)
This paper proposes an HMM training technique using multiple phonetic decision trees and evaluates it in speech recognition. In the use of context dependent models, the decision tree based context clustering is applied to find a parameter tying structure. However, the clustering is usually performed based on statistics of HMM state sequences which are(More)
This paper proposes a deterministic annealing based training algorithm for Bayesian speech recognition. The Bayesian method is a statistical technique for estimating reliable predictive distributions by marginalizing model parameters. However, the local maxima problem in the Bayesian method is more serious than in the ML-based approach, because the Bayesian(More)
This paper explores a cross-lingual speaker adaptation technique for HMM-based speech synthesis, where a source voice model for En-glish is transformed into a target speaker model using Mandarin Chinese speech data from the target speaker. A phone mapping-based method is adopted to map Chinese Initial/Finals into English phonemes and two types of mapping(More)