Two-pass decision tree construction for unsupervised adaptation of HMM-based synthesis models
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Gibson, M. (2009). Two-pass decision tree construction for unsupervised adaptation of HMM-based synthesis models. http://www.dspace.cam.ac.uk/handle/1810/226336
Hidden Markov model (HMM) -based speech synthesis systems possess several advantages over concatenative synthesis systems. One such advantage is the relative ease with which HMM-based systems are adapted to speakers not present in the training dataset. Speaker adaptation methods used in the field of HMM-based automatic speech recognition (ASR) are adopted for this task. In the case of unsupervised speaker adaptation, previous work has used a supplementary set of acoustic models to firstly estimate the transcription of the adaptation data. By defining a mapping between HMM-based synthesis models and ASR-style models, this paper introduces an approach to the unsupervised speaker adaptation task for HMM-based speech synthesis models which avoids the need for supplementary acoustic models. Further, this enables unsupervised adaptation of HMM-based speech synthesis models without the need to perform linguistic analysis of the estimated transcription of the adaptation data.
HMM-based speech synthesis, unsupervised speaker adaptation
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