Preventing violence takes an absolute necessity in our society. Whether in homes with a particular risk of domestic violence, as in prisons or schools, there is a need for systems capable of detecting risk situations, for preventive purposes. One of the most important factors that precede a violent situation is an emotional state of anger. In this paper we discuss the features that are required to provide decision makers dedicated to the detection of emotional states of anger from speech signals. For this purpose, we present a set of experiments and results with the aim of studying the combination of features extracted from the literature and their effects over the detection performance (relationship between probability of detection of anger and probability of false alarm) of a neural network and a least-square linear detector.
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