Emotion recognition using Lyapunov exponent of the Mel-frequency energy bands

This paper presents a method for emotion recognition by using LLE - Largest Lyapunov exponent of the Mel-frequency energy bands for the Romanian language. The emotion recognition for features vectors that contains LLE is better using Support Vector Machine - SVM classifier (76.4%) than Weighted K-Nearest Neighbors - WKNN classifier (72.8%). The most efficient combination was LLE with LPC - linear predictive coefficients, respectively with PARCOR - partial correlation coefficients. The best emotion recognized by using WKNN classifier is the joy state (70-80%) and the least recognized is neutral tone.