Emotion Recognition from Speech: A Classroom Experiment

Abstract

In this position paper we present an approach for the recognition of emotions from speech. Our goal is to understand the affective state of learners upon a learning process. We propose an approach that uses visual representations of the spectrum of audio segments, which are classified using the Bag-of-Visual Words model. Our approach is applied on a real-life dataset that contains interviews from middle-school students, collected upon a classroom experiment.