Speech Emotion Classification and Recognition with different methods for Turkish Language

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 (MFCC) and Mel Frequency Discrete Wavelet Coefficients (MFDWC). Moreover, spectral features of these voice samples have been obtained using Support Vector Machine (SVM). Feature vectors of the voice samples obtained have been trained with such methods as Gauss Mixture Model( GMM), Artifical Neural Network (ANN), Dynamic Time Warping (DTW), Hidden Markov Model (HMM) and hybrid model(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

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