Real-Time Recognition of Affective States from Nonverbal Features of Speech and Its Application for Public Speaking Skill Analysis

This paper presents a new classification algorithm for real-time inference of affect from nonverbal features of speech and applies it to assessing public speaking skills. The classifier identifies simultaneously occurring affective states by recognizing correlations between emotions and over 6,000 functional-feature combinations. Pairwise classifiers are constructed for nine classes from the Mind Reading emotion corpus, yielding an average cross-validation accuracy of 89 percent for the pairwise machines and 86 percent for the fused machine. The paper also shows a novel application of the classifier for assessing public speaking skills, achieving an average cross-validation accuracy of 81 percent and a leave-one-speaker-out classification accuracy of 61 percent. Optimizing support vector machine coefficients using grid parameter search is shown to improve the accuracy by up to 25 percent. The emotion classifier outperforms previous research on the same emotion corpus and is successfully applied to analyze public speaking skills.