@article{Gharavian_Sheikhan_2024, title={Emotion Recognition and Emotion Spotting Improvement Using Formant-Related Features}, volume={4}, url={https://oiccpress.com/Majlesi-Journal-of-Electrical-Engineering/article/emotion-recognition-and-emotion-spotting-improvement-using-formant-related-features/}, DOI={10.1234/mjee.v4i4.266}, abstractNote={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. }, number={4}, journal={Majlesi Journal of Electrical Engineering}, publisher={OICC Press}, author={Gharavian, Davood and Sheikhan, Mansour}, year={2024}, month={Feb.}, keywords={Quadrotor, nonlinear control, Lyapunov Stability, Genetic Algorithm, Gain Tuning. , Emotion recognition, formants, Gaussian Mixture Model} }