In this study, we utilized an improved version of the classical KNN algorithm which associates to each parameter from the features vectors weights according to their performance in the classification process. We obtained the recognition percents of emotions around 65-67%, for the Romanian language, on the SROL database, which are comparable with the results for other languages, with non-professional voice database. This is the first study when the parameters are extracted on the sentence level. Until now, the analysis was made on the phoneme level.

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The main goal of this paper is to establish the relevance of nonlinear parameters (Lyapunov exponents) in the automatic classification of emotions, for the Romanian language. The Largest Lyapunov Exponent -LLE was computed for the MFCC mel frequency cepstral coefficients and the LPCC linear prediction cepstral coefficients. The Support Vector Machine -SVM classifier provides better results than Weighted K-Nearest Neighbors -WKNN classifier in emotion recognition for feature vectors that contains LLE (around 75%). The best recognized by using SVM classifier was the neutral tone, followed by the sadness, fury and the weakest recognized was the joy. For features vectors which include LLE the best results was obtained in combination with LAR -Log Area Ratio coefficients, respectively PARCOR -partial correlation coefficients.