JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2010, Vol. 40 ›› Issue (4): 19-22.

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Speech signal representation and feature extraction based on ICA

DONG Zhi-qiang1, LIU Ju1, ZOU Xin2, DU Jun1   

  1. 1. School of Information Science and Engineering, Shandong University, Jinan 250100, China;
    2. School of Electronic, Electrical and Computer Engineering, University of Birmingham, Birmingham B152tt, UK
  • Received:2009-11-16 Online:2010-08-16 Published:2009-11-16

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Abstract:

A mathematical derivation was presented to demonstrate the effectiveness of the independent component analysis (ICA) based on signal representation for non-Gaussian speech extraction when corrupted by Gaussian noises. In experiments, the features based on ICA were applied to the speaker identification task. Experimental results showed that the performance of  features based on ICA could get obvious improvements compared with the traditional mel-frequency cepstral coefficients (MFCC) features obtained by discrete cosine transform (DCT).

Key words: independent component analysis, speech feature extraction, speaker identification

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