Prosodic, Spectral and Voice Quality Feature Selection Using a Long-Term Stopping Criterion for Audio-Based Emotion Recognition

Abstract

Emotion recognition from speech is an important field of research in human-machine-interfaces, and has begun to influence everyday life by employment in different areas such as call centers or wearable companions in the form of smartphones. In the proposed classification architecture, different spectral, prosodic and the relatively novel voice quality features are extracted from the speech signals. These features are then used to represent long-term information of the speech, leading to utterance-wise suprasegmental features. The most promising of these features are selected using a forward-selection/backward-elimination algorithm with a novel long-term termination criterion for the selection. The overall system has been evaluated using recordings from the public Berlin emotion database. Utilizing the resulted features, a recognition rate of 88,97% has been achieved which surpasses the performance of humans on this database and is comparable to the state of the art performance on this dataset.