Robust speech feature extraction based on Gabor filtering and tensor factorization

Authors:
Qiang Wu; Liqing Zhang; Guangchuan Shi
Affiliations:
Department of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China;Department of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China;Department of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

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International Journal of Speech Technology

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Abstract

In this paper, we investigate the speech feature extraction problem in the noisy environment. A novel approach based on Gabor filtering and tensor factorization is proposed. From recent physiological and psychoacoustic experimental results, localized spectro-temporal features are essential for auditory perception. We employ 2D-Gabor functions with different scales and directions to analyze the localized patches of power spectrogram, by which speech signal can be encoded as a general higher order tensor. Then Nonnegative Tensor PCA with sparse constraint is used to learn the projection matrices from multiple interrelated feature subspaces and extract the robust features. Experimental results confirm that our proposed method can improve the speech recognition performance, especially in noisy environment, compared with traditional speech feature extraction methods.