Long-Term Statistical Feature Extraction from Speech Signal and Its Application in Emotion Recognition

In this paper we propose a statistical-based parametrization framework for representing the speech through a fixed-length supervector which paves the way for capturing the long-term properties of this signal. Having a fixed-length representation for a variable-length pattern like speech which preserved the task-relevant information allows for using a wide range of powerful discriminative models which could not effectively handle the variability in the pattern length. In the proposed approach, a GMM is trained for each class and the posterior probabilities of the components of all the GMMs are computed for each data instance frame, averaged over all utterance frames and finally stacked into a supervector. The main benefits of the proposed method are making the feature extraction task-specific, performing a remarkable dimensionality reduction and yet preserving the discriminative capability of the extracted features. This method leads to an 7.6i?ź% absolute performance improvement in comparison with the baseline system which is a GMM-based classifier and results in 87.6i?ź% accuracy in emotion recognition task. Human performance on the employed database Berlin is reportedly 84.3i?ź%.