Improvement of Recognition Performance for Limabeam Algorithm by using MLLR Adaptation

Title
Improvement of Recognition Performance for Limabeam Algorithm by using MLLR Adaptation
Author(s)
정현열최숙남구엔딘쿵
Keywords
Limabeam; Calibrate Limabeam algorithm; MLLR adaptation
Issue Date
201308
Publisher
대한임베디드공학회
Citation
대한임베디드공학회논문지, v.8, no.4, pp.219 - 225
Abstract
This paper presents a method using Maximum-Likelihood Linear Regression (MLLR) adaptation to improve recognition performance of Limabeam algorithm for speech recognition using microphone array. From our investigation on Limabeam algorithm, we can see that the performance of filtering optimization depends strongly on the supporting optimal state sequence and this sequence is created by using Viterbi algorithm trained with HMM model. So we propose an approach using MLLR adaptation for the recognition of speech uttered in a new environment to obtain better optimal state sequence that support for the filtering parameters' optimal step. Experimental results show that the system embedded with MLLR adaptation presents the word correct recognition rate 2% higher than that of original calibrate Limabeam and also present 7% higher than that of Delay and Sum algorithm. The best recognition accuracy of 89.4% is obtained when we use 4 microphones with 5 utterances for adaptation.
URI
http://hdl.handle.net/YU.REPOSITORY/29114
ISSN
1975-5066
Appears in Collections:
공과대학 > 모바일정보통신공학과 > Articles
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