Comparison of different classification methods for emotion recognition

The paper presents a comparison of different classification techniques for the task of classifying a speaker's emotional state into one of two classes: aroused and normal. The comparison was conducted using the WEKA (The Waikato Environment for Knowledge Analysis) open source software which consists of a collection of machine learning algorithms for data mining. The aim of this paper is to investigate the efficiency of different classification methods to recognize the emotional state of a speaker with features obtained by a constraint version of the Maximum Likelihood Linear Regression (CMLLR). For our experiments we adopted the multi-modal AvID database of emotions, which comprises 1708 samples of utterances each lasting at least 15 seconds. The database was randomly divided into a training set and a testing set in a ratio of 5:1. Since there are much more samples in the database belonging to the neutral class than to the aroused class, the latter was over-sampled to ensure that both classes in contained equal numbers of samples in the training set. The build-in WEKA classifiers were divided into five groups based on their theoretical foundation, i.e., the group of classifiers related to the Bayes's theorem, the group of distance-based classifiers, the group of discriminant classifiers, the group of neural networks, and finally the group of decision tree classifiers. From each group we present the results of the best evaluated algorithms with respect to the unweighted average recall.