Stress/emotion classification using features extracted in compliance with the front-end of the ETSI ES 202 211 V1.1.1 standard

The paper presents the study and the performance results of a system for emotion classification using the architecture of a Distributed Speech Recognition System (DSR). The parameters used were extracted by the front-end ETSI Aurora eXtended of a mobile terminal in compliance with the ETSI ES 202 211 V1.1.1 standard. On the basis of the time trend of these parameters, over 3800 statistical parameters were extracted to characterize semantic units of two different lengths (sentences and words). Using the WEKA (Waikato Environment for Knowledge Analysis) software the most significant parameters for the classification of emotional states were selected and the results of various classification techniques were analysed. The results, obtained using both the Berlin Database of Emotional Speech (EMO-DB) and the Speech Under Simulated and Actual Stress (SUSAS) corpus, showed that the best performance is achieved using a Support Vector Machine (SVM) trained with the Sequential Minimal Optimization (SMO) algorithm, after normalizing and discretizing the input statistical parameters.