1 research outputs found
EXTENDED SPEECH EMOTION RECOGNITION AND PREDICTION
Humans are considered to reason and act rationally and that is believed to be their fundamental difference from the
rest of the living entities. Furthermore, modern approaches in the science of psychology underline that humans as a thinking
creatures are also sentimental and emotional organisms. There are fifteen universal extended emotions plus neutral emotion:
hot anger, cold anger, panic, fear, anxiety, despair, sadness, elation, happiness, interest, boredom, shame, pride, disgust,
contempt and neutral position. The scope of the current research is to understand the emotional state of a human being by
capturing the speech utterances that one uses during a common conversation. It is proved that having enough acoustic
evidence available the emotional state of a person can be classified by a set of majority voting classifiers. The proposed set of
classifiers is based on three main classifiers: kNN, C4.5 and SVM RBF Kernel. This set achieves better performance than
each basic classifier taken separately. It is compared with two other sets of classifiers: one-against-all (OAA) multiclass SVM
with Hybrid kernels and the set of classifiers which consists of the following two basic classifiers: C5.0 and Neural Network.
The proposed variant achieves better performance than the other two sets of classifiers. The paper deals with emotion
classification by a set of majority voting classifiers that combines three certain types of basic classifiers with low
computational complexity. The basic classifiers stem from different theoretical background in order to avoid bias and
redundancy which gives the proposed set of classifiers the ability to generalize in the emotion domain space