Enhancing robustness of speech recognition by approach of feature with confident weight

Authors:
Lingnan Ge;Katsuhiko Shirai;Yubo Ge
Affiliations:
School of Science and Engineering, Waseda University, Tokyo, Japan;School of Science and Engineering, Waseda University, Tokyo, Japan;Department of Mathematics Science, Tsinghua University, Beijing, China
Venue:
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Year:
2006

Citing 2
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Robust automatic speech recognition with missing and unreliable acoustic data

Speech Communication
Missing Data Techniques for Robust Speech Recognition

ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2

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Abstract

Enhancement of robustness has become one of research focuses of acoustic speech recognition system. In recent works, Missing Feature Theory (MFT) has been proved an available and considerable solution for robust speech recognition based on either ignoring or compensating the unreliable components of feature vectors corrupted mainly by band-limited background noise. Because of MFA classifying in binary way and necessarily of dealing with the cepstral feature, this paper proposes three new approaches based on confidence analysis. Approach of Feature with Confident Weight(AFCW) estimates the confidence of each feature component as its weight and describes the effect of noise in a more precise way. The other two approaches, SC(Simple Cepstral)- and TC(Total Cepstral)-AFCW, can be regarded as AFCW on cepstral domain. Experimental results show proposed approaches could improve the recognition accuracy significantly in adverse environment, including stationary and non-stationary noise environments.