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Editor-in-chief

 
Faculty of Science, University of Brunei Darussalam, Brunei Darussalam   
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IJIEE 2012 Vol.2(4): 552-555 ISSN: 2010-3719
DOI: 10.7763/IJIEE.2012.V2.159

A Combination of Maximum Likelihood Bayesian Framework and Discriminative Linear Transforms for Speaker Adaptation

Sh. Pirhosseinloo and Sh. Javadi
Abstract—Linear transforms are one of the most commonly used methods to speaker adaptation. In this paper, we present a combinational method of Bayesian framework and maximum likelihood linear regression as well as discriminative method for speaker adaptation. Furthermore significant gains can be obtained using discriminative training for acoustic models. Experiments on supervised adaptation on Persian data show that the combinational method outperforms both Maximum likelihood linear regression and Bayesian framework. Also the proposed method with discriminative adaptation outperforms previously proposed methods for transform estimation and discriminative training outperforms ML training.

Index Terms—Discriminative linear transforms, maximum-a-posterior adaptation, maximum likelihood linear regression adaptation, speech recognition, speaker adaptation.

Sh. Pirhosseinloo is the member of Scientific Association of Electrical and Electronic Engineering, Islamic Azad University Central Tehran Branch, and Tehran, Iran (e-mail: sh.pier@yahoo.com). 
Sh. Javadi is the assistant Professor of Electrical Engineering Department Islamic Azad University Central Tehran Branch, Tehran, Iran (e-mail: sh.javadi@iauctb.ac.ir).

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Cite: Sh. Pirhosseinloo and Sh. Javadi, "A Combination of Maximum Likelihood Bayesian Framework and Discriminative Linear Transforms for Speaker Adaptation," International Journal of Information and Electronics Engineering vol. 2, no. 4, pp. 552-555, 2012.

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