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Compensating Acoustic Mismatch Using Class-Based Histogram Equalization for Robust Speech Recognition

EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 067870 (2007) Cite this article

Abstract

A new class-based histogram equalization method is proposed for robust speech recognition. The proposed method aims at not only compensating for an acoustic mismatch between training and test environments but also reducing the two fundamental limitations of the conventional histogram equalization method, the discrepancy between the phonetic distributions of training and test speech data, and the nonmonotonic transformation caused by the acoustic mismatch. The algorithm employs multiple class-specific reference and test cumulative distribution functions, classifies noisy test features into their corresponding classes, and equalizes the features by using their corresponding class reference and test distributions. The minimum mean-square error log-spectral amplitude (MMSE-LSA)-based speech enhancement is added just prior to the baseline feature extraction to reduce the corruption by additive noise. The experiments on the Aurora2 database proved the effectiveness of the proposed method by reducing relative errors by over the mel-cepstral-based features and by over the conventional histogram equalization method, respectively.

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  1. School of Engineering, Information and Communications University, 119 Munjiro, Daejeon, Yuseong-Gu, 305-732, South Korea

    Youngjoo Suh, Sungtak Kim & Hoirin Kim

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  1. Youngjoo Suh
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  2. Sungtak Kim
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Correspondence to Youngjoo Suh.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/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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Suh, Y., Kim, S. & Kim, H. Compensating Acoustic Mismatch Using Class-Based Histogram Equalization for Robust Speech Recognition. EURASIP J. Adv. Signal Process. 2007, 067870 (2007). https://doi.org/10.1155/2007/67870

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