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Positive and negative emotions recognition from speech signal using acoustic and lexical features

This paper aims to present the work on emotion recognition in Indonesian spoken language. For this effort, we construct Indonesian Emotional Corpus (IDEC). In constructing the corpus, we aim at natural emotional occurrences. We choose television talk shows due to its natural emotional content. We extract two types of features from IDEC: acoustic and lexical features. We employ Support Vector Machine (SVM) and Random Forest algorithm to model the emotions utilizing these two types of features. Experiment result shows an average F-measure of 0.772 at best for 2 emotion classes based on valence dimension. We hope to continue this work and move towards more precise emotion recognizer by collecting more data, incorporating other features, and defining more complex emotion classes.