Speech Emotion Classification and Recognition with different methods for Turkish Language

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.

Speech emotion recognition systems and their security aspects

“…In this case, basic prosodic features, such as pitch and intensity extracted from audio recordings, served as input to the algorithm. Since then, many other studies have been conducted in the field of SER, in a variety of domains, ranging from single linguistic (Kryzhanovsky et al 2018;Badshah et al 2017;Lech et al 2018;Lim et al 2017;Bakir and Yuzkat 2018) to para-linguistic (Pierre-Yves 2003; Satt et al 2017;Hajarolasvadi and Demirel 2019;Khanjani et al 2021), from real-life utterances (Pierre-Yves 2003) to the recorded utterances of actors (Kryzhanovsky et al 2018;Satt et al 2017;Badshah et al 2017;Lech et al 2018;Hajarolasvadi and Demirel 2019), and from the use of digital data in the time domain (Williamson 1978) to the use of spectrograms in the frequency-time domain (Kryzhanovsky et al 2018;Satt et al 2017;Badshah et al 2017;Lech et al 2018;Hajarolasvadi and Demirel 2019).…”

Evrişimsel Sinir Ağları ile Konuşmadan Duygu Tanıma Sistemi

“…In this work (Bakır and Yuzkat, 2018), emotions are recognized using voice data. The language used in voices are Turkish.…”

Bilingual Speech Emotion Recognition Using Neural Networks: A Case Study for Turkish and English Languages

“…Some studies have been done for emotion detection in Turkish language. Bakır and Yuzkat implemented some models with the extracted features using MFCC and Mel Frequency Discrete Wavelet Coefficients (MFDWC) [5]. They used Hidden Markov Model (HMM), Gauss Mixture Model (GMM), Artificial Neural Network (ANN) and a GMM model combined with SVM.…”