Now showing items 1-3 of 3
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 ...
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 ...
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 ...