CLASSIFIER FUSION FOR EMOTION RECOGNITION FROM SPEECH

The intention of this work is the investigation of the performance of an automatic emotion recognizer using biologically motivated features, comprising perceived loudness features proposed by Zwicker, robust RASTA-PLP features, and novel long-term modulation spectrum-based features. Single classifiers using only one type of features and multi-classifier systems utilizing all three types are examined using two-classifier fusion techniques. For all the experiments the standard Berlin Database of Emotional Speech comprising recordings of seven different emotions is used to evaluate the performance of the proposed multi-classifier system. The performance is compared with earlier work as well as with human recognition performance. The results reveal that using simple fusion techniques could improve the performance significantly, outperforming other classifiers used in earlier work. The generalization ability of the proposed system is further investigated in a leave-out one-speaker experiment, uncovering a strong ability to recognize emotions expressed by unknown speakers. Moreover, similarities between earlier speech analysis and the automatic emotion recognition results were found.