TY - EJOUR
AU - Gharavian, Davood
AU - Sheikhan, Mansour
PY - 2024
DA - February
TI - Emotion Recognition and Emotion Spotting Improvement Using Formant-Related Features
T2 - Majlesi Journal of Electrical Engineering
VL - 4
L1 - https://oiccpress.com/Majlesi-Journal-of-Electrical-Engineering/article/emotion-recognition-and-emotion-spotting-improvement-using-formant-related-features/
DO - 10.1234/mjee.v4i4.266
N2 - Emotion has an important role in naturalness of man-machine communication. So, computerized emotion recognition from speech is investigated by many researchers in the recent decades. In this paper, the effect of formant-related features on improving the performance of emotion detection systems is experimented. To do this, various forms and combinations of the first three formants are concatenated to a popular feature vector and Gaussian mixture models are used as classifiers. Experimental results show average recognition rate of 69% in four emotional states and noticeable performance improvement by adding only one formant-related parameter to feature vector. The architecture of hybrid emotion recognition/spotting is also proposed based on the developed models. 
IS - 4
PB - OICC Press
KW - Quadrotor, nonlinear control, Lyapunov Stability, Genetic Algorithm, Gain Tuning. ,, Emotion recognition, formants, Gaussian Mixture Model 
EN -