Multi-class SVM for stressed speech recognition

This paper deals with a new automatic stressed recognition system based on kernel classification. We extracted advanced acoustic features from the stressed signals and employed a multi-class Support Vector Machines with different kernels to recognize speech utterances under stress. Gammatone Frequency Cepstral Coefficients are also established. The system implemented is tested using isolated words from SUSAS database with 4 classes: Neutral, Angry, Lombard and Loud. Experimental results show that the best performance is obtained when we use the auditory feature with different descriptors combination but it depends on the type of the kernel used.