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

Author

Feraru, Monica ; Zbancioc, Marius

Author_Institution

Inst. of Comput. Sci., Iaşi, Romania

fYear

2014

Firstpage

19

Lastpage

22

Abstract

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.

Keywords

emotion recognition; natural language processing; signal classification; support vector machines; LLE; LPC; Lyapunov exponent; Mel-frequency energy bands; PARCOR; Romanian language; SVM classifier; WKNN classifier; emotion recognition; features vectors; linear predictive coefficients; partial correlation coefficients; support vector machine; Band-pass filters; Emotion recognition; Feature extraction; Mel frequency cepstral coefficient; Nonlinear dynamical systems; Support vector machine classification; Mel-frequency; automatic emotion recognition; largest Lyapunov exponent;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Computers and Artificial Intelligence (ECAI), 2014 6th International Conference on

Print_ISBN

978-1-4799-5478-0

Type

conf

DOI

10.1109/ECAI.2014.7090140

Filename

7090140