This research explores the relevance of machine-driven Speech Emotion Recognition (SER) as a way to augment theatrical performances and interactions, such as controlling stage color/light, stimulating active audience engagement, actors’ interactive training, etc. It is well known that the meaning of a speech utterance arises from more than the linguistic content. Emotional affect can dramatically change meaning. As the basis for classification experiments, the authors developed the Acted Emotional Speech Dynamic Database (AESDD, which contains spoken utterances from 5 actors with 5 emotions. Several audio features and various classification techniques were implemented and evaluated using this database, as well comparing results with the Surrey Audio-Visual Expressed Emotion (SAVEE) database. The training classified was integrated into a novel application that performed live SER, fitting the needs of actor training.
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