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http://dx.doi.org/10.13064/KSSS.2017.9.2.103

Selective pole filtering based feature normalization for performance improvement of short utterance recognition in noisy environments  

Choi, Bo Kyeong (부산대학교 전자전기컴퓨터공학과)
Ban, Sung Min (SK텔레콤 AI사업단 음성인식기술팀)
Kim, Hyung Soon (부산대학교)
Publication Information
Phonetics and Speech Sciences / v.9, no.2, 2017 , pp. 103-110 More about this Journal
Abstract
The pole filtering concept has been successfully applied to cepstral feature normalization techniques for noise-robust speech recognition. In this paper, it is proposed to apply the pole filtering selectively only to the speech intervals, in order to further improve the recognition performance for short utterances in noisy environments. Experimental results on AURORA 2 task with clean-condition training show that the proposed selectively pole-filtered cepstral mean normalization (SPFCMN) and selectively pole-filtered cepstral mean and variance normalization (SPFCMVN) yield error rate reduction of 38.6% and 45.8%, respectively, compared to the baseline system.
Keywords
speech recognition; feature normalization; noisy environment; pole filtering;
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Times Cited By KSCI : 1  (Citation Analysis)
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