Automated Extraction of Features from Arabic Emotional Speech Corpus

Abstract

This paper presents the principal phase of extraction and recognition of the basic emotions in the Arabic speech applied to five emotional states were taken into effect; neutral, sadness, fear, anger and happiness. Emotional speech database REGIM_TES was created and evaluated to provide all practical experiences of extraction. The database described in section 3 has been recorded and processed in this vein. The selected descriptors in our study are; Pitch of voice, Energy, MFCCs, Formant, LPC and the spectrogram. Descriptors showed the importance of the Arabic language on the physiological events and the influence of culture on emotional behavior. Results performed in this work showed that pooling together features extracted at different sites indeed improved classification performance. A comparative study between the kernel functions has enabled us to promote the RBF kernel SVMs multiclass classifier performing the classification phase.