Physiologically Motivated Feature Extraction for Robust Automatic Speech Recognition
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2016, Vol 7, Issue 4
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.
Authors and Affiliations
Ibrahim Missaoui, Zied Lachiri
Keywords
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- EP ID EP117836
- DOI 10.14569/IJACSA.2016.070438
- Views 89
- Downloads 0
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