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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Physiologically Motivated Feature Extraction for Robust Automatic Speech Recognition

Author 1: Ibrahim Missaoui
Author 2: Zied Lachiri

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2016.070438

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 4, 2016.

  • Abstract and Keywords
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Abstract: In this paper, a new method is presented to extract robust speech features in the presence of the external noise. The proposed method based on two-dimensional Gabor filters takes in account the spectro-temporal modulation frequencies and also limits the redundancy on the feature level. The performance of the proposed feature extraction method was evaluated on isolated speech words which are extracted from TIMIT corpus and corrupted by background noise. The evaluation results demonstrate that the proposed feature extraction method outperforms the classic methods such as Perceptual Linear Prediction, Linear Predictive Coding, Linear Prediction Cepstral coefficients and Mel Frequency Cepstral Coefficients.

Keywords: Feature extraction; Two-dimensional Gabor filters; Noisy speech recognition

Ibrahim Missaoui and Zied Lachiri, “Physiologically Motivated Feature Extraction for Robust Automatic Speech Recognition” International Journal of Advanced Computer Science and Applications(IJACSA), 7(4), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070438

@article{Missaoui2016,
title = {Physiologically Motivated Feature Extraction for Robust Automatic Speech Recognition},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070438},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070438},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {4},
author = {Ibrahim Missaoui and Zied Lachiri}
}


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