Multitaper MFCC Features for Acoustic Stress Recognition from Speech
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2017, Vol 8, Issue 3
Abstract
Ameliorating the performances of speech recognition system is a challenging problem interesting recent researchers. In this paper, we compare two extraction methods of Mel Frequency Cepstral Coefficients used to represent stressed speech utterances in order to obtain best performances. The first method known as traditional is based on single window (taper) generally the Hamming window and the second one is a novel technique developed with multitapers instead of a single taper. The extracted features are then classified using the multiclass Support Vector Machines. Experimental results on the SUSAS database have shown that the multitaper MFCC features outperform the conventional MFCCs.
Authors and Affiliations
Salsabil Besbes, Zied Lachiri
Keywords
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- EP ID EP251116
- DOI 10.14569/IJACSA.2017.080361
- Views 111
- Downloads 0
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