Towards real-time speech emotion recognition for affective e-learning

Kiavash Bahreini · Rob J. Nadolski · Wim Westera

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Emotion and Mood Recognition Sentiment Analysis and Opinion Mining Intelligent Tutoring Systems and Adaptive Learning

Abstract

This paper presents the voice emotion recognition part of the FILTWAM framework for real-time emotion recognition in affective e-learning settings. FILTWAM (Framework for Improving Learning Through Webcams And Microphones) intends to offer timely and appropriate online feedback based upon learner’s vocal intonations and facial expressions in order to foster their learning. Whereas the facial emotion recognition part has been successfully tested in a previous study, the here presented study describes the development and testing of FILTWAM’s vocal emotion recognition software artefact. The main goal of this study was to show the valid use of computer microphone data for real-time and adequate interpretation of vocal intonations into extracted emotional states. The software that was developed was tested in a study with 12 participants. All participants individually received the same computer-based tasks in which they were requested 80 times to mimic specific vocal expressions (960 occurrences in total). Each individual session was recorded on video. For the validation of the voice emotion recognition software artefact, two experts annotated and rated participants’ recorded behaviours. Expert findings were then compared with the software recognition results and showed an overall accuracy of Kappa of 0.743. The overall accuracy of the voice emotion recognition software artefact is 67 % based on the requested emotions and the recognized emotions. Our FILTWAM-software allows to continually and unobtrusively observing learners’ behaviours and transforms these behaviours into emotional states. This paves the way for unobtrusive and real-time capturing of learners’ emotional states for enhancing adaptive e-learning approaches.

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References

1977The Measurement of Observer Agreement for Categorical Data 1996GPOWER: A general power analysis program 2008A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions 2005A database of German emotional speech 2010Survey on speech emotion recognition: Features, classification schemes, and databases 2003Speech emotion recognition using hidden Markov models 1992The Impact of Emotions on Learning and Achievement: Towards a Theory of Cognitive/Motivational Mediators 2005Affective Computing: A Review

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