Speech Emotion Recognition for Performance Interaction

This paper investigates the applicability of machine-driven Speech Emotion Recognition (SER) towards the augmentation of theatrical performances and interactions (e.g., controlling stage color / light, stimulating active audience engagement, actors' interactive training, etc.). For the needs of the classification experiments. the Acted Emotional Speech Dynamic Database (AESDD) is developed, containing spoken utterances by five actors in five emotions. Several audio features and various classification techniques are implemented and evaluated, based on their performance with the AESDD, while also comparing to the well-known SAVEE database. The trained classifier is integrated in a novel application that performs live SER, fitting the needs of actors' training, while simultaneously augmenting the AESDD repository.