Recognizing emotions from student speech in tutoring dialogues

Diane J. Litman · Kate Forbes-Riley

74 citations0/0 files storedIEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)

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Emotion and Mood Recognition Intelligent Tutoring Systems and Adaptive Learning Speech and dialogue systems

Abstract

We investigate the automatic classification of student emotional states in a corpus of human-human spoken tutoring dialogues. We first annotated student turns in this corpus for negative, neutral and positive emotions. We then automatically extracted acoustic and prosodic features from the student speech, and compared the results of a variety of machine learning algorithms that use 8 different feature sets to predict the annotated emotions. Our best results have an accuracy of 80.53 % and show 26.28 % relative improvement over a baseline. These results suggest that the intelligent tutoring spoken dialogue system we are developing can be enhanced to automatically predict and adapt to student emotional states.

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References

2001Emotion recognition in human-computer interaction 2003The production and recognition of emotions in speech: features and algorithms 2002Prosody-based automatic detection of annoyance and frustration in human-computer dialog 2002Combining acoustic and language information for emotion recognition 2005Recognition of negative emotions from the speech signal 2003Classifying subject ratings of emotional speech using acoustic features 2003Classifying Subject Ratings of Emotional Speech Using Acoustic Features

Cited by

2011Recognizing Affect from Linguistic Information in 3D Continuous Space 2007Acoustic Features Extraction for Emotion Recognition 2009Emotion recognition from speech: Putting ASR in the loop 2019Transfer Sparse Discriminant Subspace Learning for Cross-Corpus Speech Emotion Recognition

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