Feature selection method for real-time speech emotion recognition

Feature selection is very important step to improve the accuracy of speech emotion recognition for many applications such as speech-to-speech translation system. Thousands of features can be extracted from speech signal however which features are the most related for speaker emotional state. Until now most of related features to emotional states are not yet found. The purpose of this paper is to propose a feature selection method which have the ability to find most related features with linear or non-linear relationship with the emotional state. Most of the previous studies used either correlation between acoustic features and emotions as for feature selection or principal component analysis (PCA) as a feature reduction method. These traditional methods does not reflect all types of relations between acoustic features and emotional state. They only can find the features which have a linear relationship. However, the relationship between any two variables can be linear, nonlinear or fuzzy. Therefore, the feature selection method should consider these kind of relationship between acoustic features and emotional state. Therefore, a feature selection method based on fuzzy inference system (FIS) was proposed. The proposed method can find all features which have any kind of above mentioned relationships. Then A FIS was used to estimate emotion dimensions valence and activations. Third FIS was used to map the values of estimated valence and activation to emotional category. The experimental results reveal that the proposed features selection method outperforms the traditional methods.