Speech Emotion Classification and Recognition with different methods for Turkish Language

Year 2018, Volume: 6 Issue: 2, 122 - 128, 30.04.2018

Cigdem Bakır Mecit Yuzkat

https://doi.org/10.17694/bajece.419557

Cited By: 3

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
(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.         

Keywords

MFCC, MFDWC, emotion, HMM, hybrid model

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