Statistical Recursive Estimation Algorithms for Speaker Adaption
Wang, Shaojun
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https://hdl.handle.net/2142/80724
Description
Title
Statistical Recursive Estimation Algorithms for Speaker Adaption
Author(s)
Wang, Shaojun
Issue Date
2001
Doctoral Committee Chair(s)
Yunxin Zhao
Department of Study
Electrical Engineering
Discipline
Electrical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Electronics and Electrical
Language
eng
Abstract
The major thrust of this thesis is the development of statistical recursive estimation algorithms for speaker adaptation. In this thesis, a unified framework for recursive maximum likelihood estimation and recursive Bayesian learning is first developed and applied to direct hidden Markov model parameter estimation. Then an online Bayesian learning technique is proposed for recursive maximum a posteriori estimation of tree-structured linear regression and affine transformation parameters. This technique has the advantages of accommodating flexible forms of transformation functions as well as prior probability density functions. To balance between model complexity and best goodness of fit to adaptation data, an efficient dynamic programming based pruning algorithm is developed for selecting models using a Bayesian variant of the minimum description length (MDL) principle. Speaker adaptation experiments using a 26-letter English alphabet vocabulary were conducted, and the results confirmed effectiveness of the on-line learning framework.
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