Research on Speech Recognition Robustness Based on Gabor Filtering

doi:10.3969/j.issn.1006-2475.2018.05.005

Abstract

Abstract: In order to improve the robustness of speech recognition system, a method of extracting the acoustic features based on GBFB (spectro-temporal Gabor filter bank) is proposed, and the dimension of the GBFB is reduced by the block PCA algorithm. Finally, the feature of GBFB are compared with the feature of GFCC, MFCC and LPCC in different noise environments. The recognition rate of GBFB features is 5.35% better than GFCC features, the recognition rate of GBFB features is 7.05% better than MFCC features. Moreover， GBFB features are 9 dB lower than the LPCC recognition base. The experimental results show that the GBFB features exhibit better robustness than the traditional features of GFCC, MFCC and LPCC in the noisy environment.

Key words: speech recognition, robustness, Gabor filter, features extraction, GBFB features

CLC Number:

TN912.3

GOU Xin-ke1，2，3， XU Gao-peng1，2，3. Research on Speech Recognition Robustness Based on Gabor Filtering[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2018.05.005.

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