Speech Emotion Classification and Public Speaking Skill Assessment

Abstract. This paper presents a new classification algorithm for realtime inference of emotions from the non-verbal features of speech. It identifies simultaneously occurring emotional states by recognising correlations between emotions and features such as pitch, loudness and energy. Pairwise classifiers are constructed for nine classes from the Mind Reading emotion corpus, yielding an average cross-validation accuracy of 89% for the pairwise machines and 86% 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%. Optimisation of support vector machine coefficients is shown to improve the accuracy by up to 25%. The classifier outperforms previous research on the same emotion corpus and achieves real-time performance.

• Type: Journal
• Year: 2010
• Where: ICPR
• Authors: Tomas Pfister, Peter Robinson
• Keywords: Average Cross-validation Accuracy, Computer Vision, Emotion Corpus, ICPR 2010, Machine