Support Super-Vector Machines in Automatic Speech Emotion Recognition

Support Super-Vector Machines in Automatic Speech Emotion Recognition

In this paper, we use super-vectors in support vector machines for automatic speech emotion recognition. In our implementation, an utterance is converted to a super-vector formed by the mean vectors of a Gaussian mixture model adapted from a universal background model. The proposed method is evaluated on FAU-Aibo database which is wellknown to be used in INTERSPEECH 2009 Emotion Challenge. In the case of HMMbased dynamic modeling classifier, we achieve an unweighted average (UA) recall rate of 40.0%, over a baseline of 35.5%, by using the delta features and increasing the number of mixture components. In the case of SVM-based static modeling classifier, we achieve an unweighted average (UA) recall rate of 38.9%, over a baseline of 38.2%, by using the proposed super-vectors.