Balkan Journal of Electrical and Computer Engineering 2147-284X 2147-284X MUSA YILMAZ 10.17694/bajece.419557 Engineering Mühendislik Speech Emotion Classification and Recognition with different methods for Turkish Language Bakır Cigdem Yuzkat Mecit 04 30 2018 6 2 122 128 08 22 2015 11 16 2017 Copyright © 2013, Balkan Journal of Electrical and Computer Engineering 2013 Balkan Journal of Electrical and Computer Engineering

In severalapplication, emotion  recognition fromthe speech signal has been research topic since many years. To determine theemotions from the speech signal, many systems have been developed. To solve thespeaker emotion recognition problem, hybrid model is proposed to classify fivespeech emotions, including  anger,sadness, fear, happiness and neutral. The aim this study of was to actualizeautomatic voice and speech emotion recognition system using hybrid model takingTurkish sound forms and properties into consideration.  Approximately 3000 Turkish voice samples ofwords and clauses with differing lengths have been collected from 25 malesand  25 females. In this study, anauthentic and unique  Turkish  database has been used. Features of thesevoice 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). Featurevectors of the voice samples obtained have been trained with such methods asGauss 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 bycombining with SVM and GMM.  In firststage 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 beenformed from these spectral features. In the test phase, owner of a given voicesample has been identified taking the trained voice samples into consideration.Results and performances of the algorithms employed in the study forclassification have been also demonstrated in a comparative manner.         

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