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Model Compensation Approach Based on Nonuniform Spectral Compression Features for Noisy Speech Recognition

EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 032546 (2007) Cite this article

Abstract

This paper presents a novel model compensation (MC) method for the features of mel-frequency cepstral coefficients (MFCCs) with signal-to-noise-ratio- (SNR-) dependent nonuniform spectral compression (SNSC). Though these new MFCCs derived from a SNSC scheme have been shown to be robust features under matched case, they suffer from serious mismatch when the reference models are trained at different SNRs and in different environments. To solve this drawback, a compressed mismatch function is defined for the static observations with nonuniform spectral compression. The means and variances of the static features with spectral compression are derived according to this mismatch function. Experimental results show that the proposed method is able to provide recognition accuracy better than conventional MC methods when using uncompressed features especially at very low SNR under different noises. Moreover, the new compensation method has a computational complexity slightly above that of conventional MC methods.

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Authors and Affiliations

  1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510640, China

    Geng-Xin Ning, Gang Wei & Kam-Keung Chu

Authors
  1. Geng-Xin Ning
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  2. Gang Wei
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  3. Kam-Keung Chu
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Correspondence to Geng-Xin Ning.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Ning, GX., Wei, G. & Chu, KK. Model Compensation Approach Based on Nonuniform Spectral Compression Features for Noisy Speech Recognition. EURASIP J. Adv. Signal Process. 2007, 032546 (2007). https://doi.org/10.1155/2007/32546

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