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.

[1]  Ning An,et al.  Speech Emotion Recognition Using Fourier Parameters , 2015, IEEE Transactions on Affective Computing.

[2]  Hichem Karray,et al.  Automated Extraction of Features from Arabic Emotional Speech Corpus , 2016 .

[3]  Xi Li,et al.  Stress and Emotion Classification using Jitter and Shimmer Features , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[4]  Fu Wang,et al.  Decision tree SVM model with Fisher feature selection for speech emotion recognition , 2019, EURASIP J. Audio Speech Music. Process..

[5]  Shashidhar G. Koolagudi,et al.  SVM Scheme for Speech Emotion Recognition using MFCC Feature , 2013 .

[6]  Ann Franchesca Laguna,et al.  Experiments on automatic language identification for philippine languages using acoustic Gaussian Mixture Models , 2014, 2014 IEEE REGION 10 SYMPOSIUM.

[7]  Albino Nogueiras,et al.  Speech emotion recognition using hidden Markov models , 2001, INTERSPEECH.

[8]  Ziad Osman,et al.  Ensemble Models for Enhancement of an Arabic Speech Emotion Recognition System , 2019 .

[9]  Margaret Lech,et al.  Effects of band reduction and coding on speech emotion recognition , 2016, 2016 10th International Conference on Signal Processing and Communication Systems (ICSPCS).