First approach to continuous tracking of emotional temperature

A wide range of new applications can arise from the emotional state assessment obtained from speech signal, which represents a marked improvement in the human-machine interfaces and becomes an important research area in the last years. The study of emotions is not a trivial task and involves a degree of difficulty. The great majority of researches on speech emotion recognition have been made on the basis of record repositories consisting short sentences recorded in laboratory conditions. In this work we propose a strategy, previously validated under the conditions described above, for continuous tracking in long-term samples of speech in which there are emotional changes during the speech. This strategy uses a few prosodic and paralinguistic features set obtained from a temporal segmentation of the speech signal, which is more appropriate in real-world scenarios. In this paper a simple and effective method of automatic discrimination between positive and negative emotional intensity speech, named Emotional Temperature, is presented. This strategy is robust, offers low computational cost, ability to detect emotional changes and improves the performance of a segmentation based on linguistic aspects.