Multitaper MFCC Features for Acoustic Stress Recognition from Speech

Abstract

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.

Diagnosis of Parkinson’s Disease based on Wavelet Transform and Mel Frequency Cepstral Coefficients

“…The acoustic treatment has been used recently in the diagnosis of many diseases. The MFCC for the extraction of cepstral coefficients has been used in the identification of diseases in newborns by Yasmina Kheddache and Chakib Tadj [2] also Takaya Taguchi et al [3] for the major depressive disorder discrimination and for stress recognition from speech Salsabil Besbes and Zied Lachiri work with a multitaper MFCC features [4], whereas Zied Lachiri had also works on emotion recognition [5,6]. Always at the acoustic treatment we found also Nawel SOUISSI and Adnane CHERIF they work on voice disorders identification [7].…”