Using the Lyapunov exponent from cepstral coefficients for automatic emotion recognition

Abstract

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.

Speech Emotion Recognition Application for Education

“…In recent years, the research on emotion features is constantly enriched. For example, Zbancioc et al apply improved MFCC and LPCC features to emotion recognition, and the recognition rate reaches 75% [2]. Sun Ying et al extracted the nonlinear geometric features and optimized the feature parameters to obtain the optimal nonlinear features.…”

Nonlinear Dynamic Feature Extraction Based on Phase Space Reconstruction for the Classification of Speech and Emotion

“…With recent development in nonlinear analysis methods, they have been successfully applied in various fields [7][8][9][10][11][12]. Zbancioc [7] applied the Lyapunov index for the extraction of spectral coefficients of MFCC and LPCC features and achieved an emotion recognition accuracy of 75%; Firoozet al [8] evaluated nonlinear dynamic features by reconstruction of speech signals using phase space reconstruction to improve the accuracy of automatic speech recognition. Spanish researcher Karmele Lopez applied the study of the chaotic characteristic of natural speech for the detection of Alzheimer's disease and pointed out detection of the speaker's lesions by extracting the fractal dimension features in natural speech [9,10].…”