Model-based parametric features for emotion recognition from speech

Automatic emotion recognition from speech is desirable in many applications relying on spoken language processing. Telephone-based customer service systems, psychological healthcare initiatives, and virtual training modules are examples of real-world applications that would significantly benefit from such capability. Traditional utterance-level emotion recognition relies on a global feature set obtained by computing various statistics from raw segmental and supra-segmental measurements, including fundamental frequency (F0), energy, and MFCCs. In this paper, we propose a novel, model-based parametric feature set that better discriminates between the competing emotion classes. Our approach relaxes modeling assumptions associated with using global statistics (e.g. mean, standard deviation, etc.) of traditional segment-level features for classification, and results in significant improvements over the state-of-the-art in 7-way emotion classification accuracy on the standard, freely-available Berlin Emotional Speech Corpus. These improvements are consistent even in a reduced feature space obtained by Fisher's Multiple Linear Discriminant Analysis, demonstrating the signficantly higher discriminative power of the proposed feature set.