Acoustic emotion recognition based on fusion of multiple feature-dependent deep Boltzmann machines

In this paper, we present a method to improve the classification recall of a deep Boltzmann machine (DBM) on the task of emotion recognition from speech. The task involves the binary classification of four emotion dimensions such as arousal, expectancy, power, and valence. The method consists of dividing the features of the input data into separate sets and training each set individually using a deep Boltzmann machine algorithm. Afterwards, the results from each set are fused together using simple fusion. The final fused scores are compared to scores obtained from support vector machine (SVM) classifiers and from the same DBM algorithm on the full feature set. The results show that the proposed method can improve the performance of classification of four dimensions and is suitable for classification of unbalanced data sets.