Emotion recognition in Arabic speech

The general objective of this paper is to build a system in order to automatically recognize emotion in speech. The linguistic material used is a corpus of Arabic expressive sentences phonetically balanced. The dependence of the system on speaker is an encountered problem in this field; in this work we will study the influence of this phenomenon on our result. The targeted emotions are joy, sadness, anger and neutral. After an analytical study of a large number of speech acoustic parameters, we chose the cepstral parameters, their first and second derivatives, the Shimmer, the Jitter and the duration of the sentence. A classifier based on a multilayer perceptron neural network to recognize emotion on the basis of the chosen feature vector that has been developed. The recognition rate could reach more than 98% in the case of an intra-speaker classification and 54.75% in inter-speaker classification. We can see the system’s dependence on speaker clearly.