Speech emotion recognition from Indonesian spoken language using acoustic and lexical features

This paper describes our works to extend the previous work on emotion recognition for Indonesian spoken language. In this research, we construct an Indonesian emotional corpus (IDEC). In constructing the corpus, we aim at natural emotional occurrences from television talk shows. IDEC is utilized to construct the emotion recognizer using two main features, acoustic and lexical features. The Support Vector Machine (SVM), Random Forest (RF), and Multinomial Naive Bayes (MNB) algorithms are employed to model the emotions. Experiment result shows that SVM outperforms the RF and MNB algorithms. It achieves an average F- measure of 0.713 for 6 emotion classes by combining both acoustic and lexical features.