Speaker dependent emotion recognition using prosodic supervectors

This work presents a novel approach for detection of emotions embedded in the speech signal. The proposed approach works at the prosodic level, and models the statistical distribution of the prosodic features with Gaussian Mixture Models (GMM) mean-adapted from a Universal Background Model (UBM). This allows the use of GMM-mean supervectors, which are classified by a Support Vector Machine (SVM). Our proposal is compared to a popular baseline, which classifies with an SVM a set of selected prosodic features from the whole speech signal. In order to measure the speaker inter-variability, which is a factor of degradation in this task, speaker dependent and speaker independent frameworks have been considered. Experiments have been carried out under the SUSAS subcorpus, including real and simulated emotions. Results shows that in a speaker dependent framework our proposed approach achieves a relative improvement greater than 14% in Equal Error Rate (EER) with respect to the baseline approach. The relative improvement is greater than 17% when both approaches are combined together by fusion with respect to the baseline.