Efficient Recognition of Human Emotional States from Audio Signals

Automatic recognition of human emotional states is an important task for efficient human-machine communication. Most of existing works focus on the recognition of emotional states using audio signals alone, visual signals alone, or both. Here we propose empirical methods for feature extraction and classifier optimization that consider the temporal aspects of audio signals and introduce our framework to efficiently recognize human emotional states from audio signals. The framework is based on the prediction of input audio clips that are described using representative low-level features. In the experiments, seven (7) discrete emotional states (anger, fear, boredom, disgust, happiness, sadness, and neutral) from EmoDB dataset, are recognized and tested based on nineteen (19) audio features (15 standalone, 4 joint) by using the Support Vector Machine (SVM) classifier. Extensive experiments have been conducted to demonstrate the effect of feature extraction and classifier optimization methods to the recognition accuracy of the emotional states. Our experiments show that, feature extraction and classifier optimization procedures lead to significant improvement of over 11% in emotion recognition. As a result, the overall recognition accuracy achieved for seven emotions in the EmoDB dataset is 83.33% compared to the baseline accuracy of 72.22%.