Reconocimiento automático de voz emotiva con memorias asociativas Alfa-Beta SVM

one of the research lines of interest and more growth at present, within the area of voice processing is automatic emotion recognition. it is vitally important the study of speech signal not only to extract information about what is being said, but how is being said, this in order to be closer to the human-machine interaction. in literature the procedure of automatic emotion recognition consists of two stager, the first is the extraction of parameters from the voice signal and the second is the choice of model for the classification task, the problem that currently exists is not yet identified the most representative parameters of the problem nor has found the best classifier for the task, but have not yet been tested several models, this paper presents a two-dimensional representation of energy as data entry for alpha-beta associative machines svm (support vector machine) for the classification of emotions.