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Feature selection is one of the important aspects that contribute most to the emotion recognition system performance apart from the database and the classification technique used. Based on the previous finding, Mel Frequency Cepstral Coefficients (MFCC) are said to be good for emotion recognition purpose. This paper discusses the use of MFCC features to recognize human emotion on Berlin database in the German language. Global features are extracted from MFCC and tested with three classification methods; Naive Bayes, Artificial Neural Network (ANN) and Support Vector Machine (SVM). We investigate the capabilities of MFCC global features using 13, 26 and 39-dimensional cepstral features in recognizing emotions from speech. The result from the experiment will be further discussed in this paper.

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In this paper, a comparison of emotion classification undertaken by the Support Vector Machine (SVM) and the Multi-Layer Perceptron (MLP) Neural Network, using prosodic and voice quality features extracted from the Berlin Emotional Database, is reported. The features were extracted using PRAAT tools, while the WEKA tool was used for classification. Different parameters were set up for both SVM and MLP, which are used to obtain an optimized emotion classification. The results show that MLP overcomes SVM in overall emotion classification performance. Nevertheless, the training for SVM was much faster when compared to MLP. The overall accuracy was 76.82% for SVM and 78.69% for MLP. Sadness was the emotion most recognized by MLP, with accuracy of 89.0%, while anger was the emotion most recognized by SVM, with accuracy of 87.4%. The most confusing emotions using MLP classification were happiness and fear, while for SVM, the most confusing emotions were disgust and fear.