Speech emotion classification and public speaking skill assessment

Authors:
Tomas Pfister;Peter Robinson
Affiliations:
University of Cambridge, Computer Laboratory, Cambridge, UK;University of Cambridge, Computer Laboratory, Cambridge, UK
Venue:
HBU'10 Proceedings of the First international conference on Human behavior understanding
Year:
2010

Citing 4
Cited 3

The nature of statistical learning theory

The nature of statistical learning theory
Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper

Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
The WEKA data mining software: an update

ACM SIGKDD Explorations Newsletter
New frameworks to boost feature selection algorithms in emotion detection for improved human-computer interaction

BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence

Challenges of human behavior understanding

HBU'10 Proceedings of the First international conference on Human behavior understanding
Multimodal affect recognition in intelligent tutoring systems

ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Paralinguistics in speech and language-State-of-the-art and the challenge

Computer Speech and Language

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper presents a new classification algorithm for real-time 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.