Application of Feature Subset Selection Based on Evolutionary Algorithms for Automatic Emotion Recognition in Speech

The study of emotions in human-computer interaction is a growing research area. Focusing on automatic emotion recognition, work is being performed in order to achieve good results particularly in speech and facial gesture recognition. In this paper we present a study performed to analyze different machine learning techniques validity in automatic speech emotion recognition area. Using a bilingual affective database, different speech parameters have been calculated for each audio recording. Then, several machine learning techniques have been applied to evaluate their usefulness in speech emotion recognition. In this particular case, techniques based on evolutive algorithms (EDA) have been used to select speech feature subsets that optimize automatic emotion recognition success rate. Achieved experimental results show a representative increase in the abovementioned success rate.