Voice Activity Detection Based on Fisher Linear Discriminant Analysis

Wang Ming-he; Zhang Er-hua; Tang Zhen-min; Xu Hao

doi:10.11999/JEIT141122

Volume 37 Issue 6

Jun. 2015

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Wang Ming-he, Zhang Er-hua, Tang Zhen-min, Xu Hao. Voice Activity Detection Based on Fisher Linear Discriminant Analysis[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1343-1349. doi: 10.11999/JEIT141122

Citation:

Wang Ming-he, Zhang Er-hua, Tang Zhen-min, Xu Hao. Voice Activity Detection Based on Fisher Linear Discriminant Analysis[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1343-1349. doi: 10.11999/JEIT141122

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Voice Activity Detection Based on Fisher Linear Discriminant Analysis

doi: 10.11999/JEIT141122

Received Date: 2014-08-29
Rev Recd Date: 2014-12-19
Publish Date: 2015-06-19

Abstract

Abstract

Traditional Voice Activity Detection (VAD) approaches can not effectively detect consonant as well as noisy unvoiced consonant. To address this problem, this paper proposes a VAD approach Mel Frequency Cepstrum Coefficient (F-MFCC) based on Fisher linear discriminant analysis, in consideration of two-class issue regarding to consonant and background noise. Fisher criterion rule is used to solve the optimal projection vector, building upon which we can minimize the within-class scatter can be minimized and the between-class scatter can be maximized, as a result to enhance separability between consonant and background noise. Extensive experiments are conducted to evaluate the F-MFCC performance. The results demonstrate that, under different SNR and noise conditions, the proposed approach achieves higher VAD accuracy.
- Speech processing,
- Voice Activity Detection (VAD),
- Mel Frequency Cepstrum Coefficient (MFCC),
- Fisher linear discriminant analysis

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References

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