On-line hierarchical transformation of hidden Markov models for speaker adaptation
نویسنده
چکیده
This paper presents a novel framework of on-line hierarchical transformation of hidden Markov models (HMM’s) for speaker adaptation. Our aim is to incrementally transform (or adapt) all the HMM parameters to a new speaker even though part of HMM units are unseen in adaptation data. The transformation paradigm is formulated according to the approximate Bayesian estimate, which the prior statistics and the transformation parameters are incrementally updated for each consecutive adaptation data. Using this formulation, the updated prior statistics and the current block of data are sufficient for on-line transformation. Further, we establish a hierarchical tree of HMM’s and use it to dynamically control the transformation sharing for each HMM unit. In the speaker adaptation experiments, we demonstrate the superiority of proposed on-line transformation to other method.
منابع مشابه
Online hierarchical transformation of hidden Markov models for speech recognition
This paper proposes a novel framework of online hierarchical transformation of hidden Markov model (HMM) parameters for adaptive speech recognition. Our goal is to incrementally transform (or adapt) all the HMM parameters to a new acoustical environment even though most of HMM units are unseen in observed adaptation data. We establish a hierarchical tree of HMM units and apply the tree to dynam...
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