Feature space dimension reduction in speech emotion recognition using support vector machine

We report implementations of automatic speech emotion recognition systems based on support vector machines in this paper. While common systems often extract a very large feature set per utterance for emotion classification, we conjecture that the dimension of the feature space can be greatly reduced without severe degradation of accuracy. Consequently, we systematically reduce the number of features via feature selection and principal component analysis. The evaluation is carried out on the Berlin Database of Emotional Speech, also known as EMO-DB, which consists of 10 speakers and 7 emotions. The results show that we can trim the feature set to 37 features and still maintain an accuracy of 80%. This means a reduction of more than 99% compared to the baseline system which uses more than 6,000 features.