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Maximum Likelihood Training and Adaptation of Embedded Speech Recognizers for Mobile Environments

Authors
Cho, YoungkyuYook, Dongsuk
Issue Date
Feb-2010
Publisher
WILEY
Keywords
Embedded speech recognition; maximum likelihood distribution clustering (MLDC); quantized HMM
Citation
ETRI JOURNAL, v.32, no.1, pp.160 - 162
Indexed
SCIE
SCOPUS
KCI
Journal Title
ETRI JOURNAL
Volume
32
Number
1
Start Page
160
End Page
162
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/117019
DOI
10.4218/etrij.10.0209.0242
ISSN
1225-6463
Abstract
For the acoustic models of embedded speech recognition systems, hidden Markov models (HMMs) are usually quantized and the original full space distributions are represented by combinations of a few quantized distribution prototypes. We propose a maximum likelihood objective function to train the quantized distribution prototypes. The experimental results show that the new training algorithm and the link structure adaptation scheme for the quantized HMMs reduce the word recognition error rate by 20.0%.
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