Emotion Recognition from Speech Signal in Multilingual Experiments

Abstract

Emotion recognition from speech signal has become more and more important in advanced human-machine applications. The detailed description of emotions and their detection play an important role in the psychiatric studies but also in other fields of medicine such as anamnesis, clinical studies or lie detection. In this paper some experiments using multilingual emotional databases are presented. For the features extracted from the speech material, the LPC (Linear predictive coding), LPCC (Linear Predictive Cepstral Coefficients) and MFCC (Mel Frequency Cepstral Coefficients) coefficients are employed. The Weka tool was used for the classification task, selecting the k-NN (k-nearest neighbors) and SVM (Support Vector Machine) classifiers. The results for the selected features vectors show that the emotion recognition rate is satisfactory when multilingual speech material is used for training and testing. When the training is made using emotional materials for a language and testing with materials in other language the results are poor. Therefore, this shows that the features extracted from speech display a closed dependency with the spoken language.