Maximum Likelihood Training and Adaptation of Embedded Speech Recognizers for Mobile Environments
- Authors
- Cho, Youngkyu; Yook, 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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Collections - Graduate School > Department of Computer Science and Engineering > 1. Journal Articles
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