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.

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}
}