Classification of speech under stress based on cepstral features and one-class SVM
Salsabil Besbes, Zied Lachiri · 2017
This paper presents an approach that aims to recognize stressed speech utterances. Our work consists of extracting features using Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC). Indeed, these features are classified with One-class Support Vector Machines (OC-SVM). The results of the proposed method are obtained by conducting speech samples of four stressed states from the SUSAS database. The system provides good performances with accuracy rate exceeding 98% with the different features extracted from the stressed database. A comparison between the classification accuracies obtained with OC-SVM and those given when we apply other multiclass Support Vectors Machines approaches is also presented.