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Supervised Vocal-Based Emotion Recognition Using Multiclass Support Vector Machine, Random Forests, and Adaboost
(AUDIO ENGINEERING SOC, 2017-07)
This paper investigates and compares three different classifiers-multi-class Support Vector Machine, Adaboost, and random forests-for the purpose of vocal emotion recognition. Additionally, the decisions of all classifiers ...
Virtual Reality and Its Applications in Education: Survey
In the education process, students face problems with understanding due to the complexity, necessity of abstract thinking and concepts. More and more educational centres around the world have started to introduce powerful ...
Efficiency of chosen speech descriptors in relation to emotion recognition
This research paper presents parametrization of emotional speech using a pool of common features utilized in emotion recognition such as fundamental frequency, formants, energy, MFCC, PLP, and LPC coefficients. The pool ...
Vocal-based emotion recognition using random forests and decision tree
This paper proposes a new vocal-based emotion recognition method using random forests, where pairs of the features on the whole speech signal, namely, pitch, intensity, the first four formants, the first four formants ...
Emotion Recognition from Skeletal Movements
Automatic emotion recognition has become an important trend in many artificial intelligence (AI) based applications and has been widely explored in recent years. Most research in the area of automated emotion recognition ...