Speech Emotion Classification and Recognition with different methods for Turkish Language

Authors : Cigdem BAKIR, Mecit YUZKAT

Pages : 122-128

Doi:10.17694/bajece.419557

View : 16 | Download : 13

Publication Date : 2018-04-30

Article Type : Research Paper

Abstract :In several application, emotion  recognition from the speech signal has been research topic since many years. To determine the emotions from the speech signal, many systems have been developed. To solve the speaker emotion recognition problem, hybrid model is proposed to classify five speech emotions, including  anger, sadness, fear, happiness and neutral. The aim this study of was to actualize automatic voice and speech emotion recognition system using hybrid model taking Turkish sound forms and properties into consideration.  Approximately 3000 Turkish voice samples of words and clauses with differing lengths have been collected from 25 males and  25 females. In this study, an authentic and unique  Turkish  database has been used. Features of these voice samples have been obtained using Mel Frequency Cepstral Coefficients insert ignore into journalissuearticles values(MFCC); and Mel Frequency Discrete Wavelet Coefficients insert ignore into journalissuearticles values(MFDWC);. Moreover, spectral features of these voice samples have been obtained  using Support Vector Machine insert ignore into journalissuearticles values(SVM);. Feature vectors of the voice samples obtained have been trained with such methods as Gauss Mixture Modelinsert ignore into journalissuearticles values( GMM);, Artifical Neural Network insert ignore into journalissuearticles values(ANN);, Dynamic Time Warping insert ignore into journalissuearticles values(DTW);, Hidden Markov Model insert ignore into journalissuearticles values(HMM); and hybrid modelinsert ignore into journalissuearticles values(GMM with combined SVM);.  This hybrid model has been carried out by combining with SVM and GMM.  In first stage of this model, with SVM has been performed  subsets obtained vector of  spectral features. In the second  phase, a set of training and tests have been formed from these spectral features. In the test phase, owner of a given voice sample has been identified taking the trained voice samples into consideration. Results and performances of the algorithms employed in the study for classification have been also demonstrated in a comparative manner.         
Keywords : MFCC, MFDWC, emotion, HMM, hybrid model