Abstract
Recognizing emotions from speech using machine learning algorithms has become an active research topic lately as a result of the demand for more human interactive applications. Emotion recognition systems are mostly implemented in German, English, Spanish, Dutch, Danish, and other European and Asian languages due to the availability of datasets for these languages. However, for Arabic, there is an extremely limited number of available speech emotion datasets. Therefore, in this paper studies emotion recognition based on Arabic Saudi dialect spoken data. The dataset was created from freely available YouTube videos and labeled using four perceived emotions: anger, happiness, sadness, and neutral. Various spectral features such as the mel-frequency cepstral coefficient (MFCC) and mel spectrogram, were extracted, and then the classification methods support vector machine (SVM), multi-layer perceptron (MLP), and k-nearest neighbor (KNN) were applied. The results were discussed, analyzed, and compared between the three models using different feature extractions. Experiments showed that SVM obtained the best accuracy result with 77.14%, demonstrating improvement in Arabic speech emotion recognition for this classification method.