Title :
Speech emotion recognition using RBF kernel of LIBSVM
Author :
Chavhan, Y.D. ; Yelure, B.S. ; Tayade, K.N.
Author_Institution :
GCE, Karad, Karad, India
Abstract :
Automatic Speech Emotion Recognition (SER) is a current research topic in the field of Human Computer Interaction (HCI) with wide range of applications. The speech features such as, Mel Frequency cepstrum coefficients (MFCC) and Mel Energy Spectrum Dynamic Coefficients (MEDC) are extracted from speech utterance. The LIBSVM is used as classifier to identify different emotional states such as anger, happiness, sadness, neutral, fear, from Berlin emotional database. The results are taken by using RBF kernel of LIBSVM. It gives 93.75% recognition accuracy for RBF kernel.
Keywords :
cepstral analysis; emotion recognition; feature extraction; human computer interaction; radial basis function networks; regression analysis; signal classification; speech recognition; support vector machines; Berlin emotional database; HCI; LIBSVM; MEDC; MFCC; RBF kernel; SER; anger; automatic speech emotion recognition; emotional states; fear; happiness; human computer interaction; mel energy spectrum dynamic coefficients; mel frequency cepstrum coefficients; neutral; sadness; speech feature extraction; speech utterance; Emotion recognition; Feature extraction; Kernel; Mel frequency cepstral coefficient; Speech; Speech recognition; Support vector machines; Emotion Recognition; LIBSVM; MFCC and MEDC; RBF; Speech emotion;
Conference_Titel :
Electronics and Communication Systems (ICECS), 2015 2nd International Conference on
Conference_Location :
Coimbatore
Print_ISBN :
978-1-4799-7224-1
DOI :
10.1109/ECS.2015.7124760
