|Title: ||Two-pass decision tree construction for unsupervised adaptation of HMM-based synthesis models|
|Authors: ||Gibson, Matthew|
|Keywords: ||HMM-based speech synthesis|
unsupervised speaker adaptation
|Issue Date: ||2009|
|Citation: ||Proceedings Interspeech 2009, pp 1791-1794|
|Abstract: ||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.|
|Appears in Collections:||Scholarly works - Information Engineering|
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