Speech feature extraction based on wavelet modulation scale for robust speech recognition

Authors:
Xin Ma;Weidong Zhou;Fang Ju;Qi Jiang
Affiliations:
College of Information Science and Engineering, Shandong University, Jinan, Shandong, P.R. China;College of Information Science and Engineering, Shandong University, Jinan, Shandong, P.R. China;College of Information Science and Engineering, Shandong University, Jinan, Shandong, P.R. China;College of Control Science and Engineering, Shandong University, Jinan, Shandong, P.R. China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

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Abstract

An analysis based on wavelet modulation scales feature extraction is proposed. Considering human auditory perception and varieties of disturbances, instead of the frequency differences, wavelet modulation scales are adopted to reflect the dynamic features of speech in ASR. Experiments for the Chinese digit-string recognition show extracting the wavelet modulation scales as the dynamic features have good performance both in additional noises and convolutional noises environment.