2026-09-19T08:04:51Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/80724 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Yunxin Zhao Wang, Shaojun 2015-09-25T20:07:50Z 2015-09-25T20:07:50Z 10000-01-01 2001 2001 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. Made available in DSpace on 2015-09-25T20:07:50Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3017245.pdf: 6132824 bytes, checksum: a825e3dd582f17016e3c030efa5ee2b7 (MD5) Previous issue date: 2001 Embargo set by: Seth Robbins for item 82006 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 127 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2001. http://hdl.handle.net/2142/80724 (MiAaPQ)AAI3017245 eng Engineering, Electronics and Electrical Statistical Recursive Estimation Algorithms for Speaker Adaption text Electrical Engineering Electrical Engineering University of Illinois at Urbana-Champaign Dissertation Ph.D.