Abstract
This study investigates the emotion-discriminant ability of acoustic cues from speech collected in the automatic computer tutoring system named as Auto Tutor. The purpose of this study is to examine the acoustic cues for emotion detection of the speech channel from the learning system, and to compare the emotion-discriminant performance of acoustic cues (in this study) with the conversational cues (available in previous work). Comparison between the classification performance obtained using acoustic cues and conversational cues shows that the emotions: flow and boredom are better captured in acoustics than conversational cues while conversational cues play a more important role in multiple-emotion classification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Litman, D.J., Forbes-Riley, K.: Recognizing student emotions and attitudes on the basis of utterances in spoken tutoring dialogues with both human and computer tutors. Speech Communication 48, 559–590 (2006)
D’Mello, S., Graesser, A.: Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features. User Modeling and User-Adapted Interaction 20, 147–187 (2010)
Fragopanagos, N., Taylor, J.G.: Emotion recognition in human-computer interaction. Neural Networks 18, 389–405 (2005)
Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., Taylor, J.G.: Emotion recognition in human-computer interaction. IEEE Signal Processing Magazine 18, 32–80 (2001)
D’Mello, S., Graesser, A.: Automatic detection of learners emotions from gross body language. Applied Artificial Intelligence 23, 123–150 (2009)
D’Mello, S., Craig, S.D., Witherspoon, A., McDaniel, B., Graesser, A.: Automatic detection of learners affect from conversational cues. User Modeling and User-Adapted Interaction 18, 45–80 (2008)
McKeown, G., Valstar, M.F., Cowie, R., Pantic, M.: The semaine corpus of emotionally coloured character interactions. In: 2010 IEEE International Conference on Multimedia and Expo. (ICME), pp. 1079–1084 (2010)
Busso, C., Sungbok, L., Narayanan, S.: Analysis of emotionally salient aspects of fundamental frequency for emotion detection. IEEE Transactions on Audio, Speech, and Language Processing 17, 582–596 (2009)
Moore, E., Clements, M.A., Peifer, J.W., Weisser, L.: Critical analysis of the impact of glottal features in the classification of clinical depression in speech. IEEE Transactions on Biomedical Engineering 55, 96–107 (2008)
Sun, R., Moore, E., Torres, J.: Investigating glottal parameters for differentiating emotional categories with similar prosodics. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2009, Taipei, Taiwan (2009)
Graesser, A., D’Mello, S., Chipman, P., King, B., McDaniel, B.: Exploring relationship between affect and learning with autotutor. In: The 13th International Conference on Artificial Intelligence in Education, pp. 16–23 (2007)
Cummings, K.E., Clements, M.A.: Analysis of the glottal excitation of emotionally styled and stressed speech. The Journal of the Acoustical Society of America 98, 88–98 (1995)
Moore, E., Clements, M., Peifer, J., Weisser, L.: Investigating the role of glottal features in classifying clinical depression. In: Proceedings of the 25th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society, vol. 3, pp. 2849–2852 (2003)
Moore, E., Torres, J.: A performance assessment of objective measures for evaluating the quality of glottal waveform estimates. In: Speech Communication (2007) (in press)
Patrick, A.N., Anastasis, K., Jon, G., Mike, B.: Estimation of glottal closure instants in voiced speech using the dypsa algorithm. IEEE Transactions on Audio, Speech, and Language Processing 15, 34–43 (2007)
Moore, E., Torres, J.: A performance assessment of objective measures for evaluating the quality of glottal waveform estimates. In: Speech Communication (2007) (in press)
Airas, M., Pulakka, H., Backstrom, T., Alku, P.: A toolkit for voice inverse filtering and parametrisation. In: Interspeech (2005)
Laukkanen, A.M., Vilkman, E., Alku, P., Oksanen, H.: Physical variations related to stress and emotional state: a preliminary study. Journal of Phonetics 24, 313–335 (1996)
Titze, I.R., Sundberg, J.: Vocal intensity in speakers and singers. The Journal of the Acoustical Society of America 91, 2936–2946 (1992)
Childers, D.G.: Vocal quality factors: Analysis, synthesis, and perception. The Journal of the Acoustical Society of America 90, 2394–2410 (1991)
Eyben, F., Wollmer, M., Schuller, B.: Openear - introducing the munich open-source emotion and affect recognition toolkit. In: 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009, pp. 1–6 (2009)
Schuller, B., Steidl, S., Batliner, A., Schiel, F., Krajewski, J.: The interspeech 2011 speaker state challenge. In: Interspeech, Italy (2011)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: An update. SIGKDD Explorations 11 (2009)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: The Thirteenth Interantional Conference on Machine Learning (1996)
Witten, I.H., Freank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2005)
Hirschberg, J., Liscombe, J., Venditti, J.: Experiments in emotional speech. In: ISCA and IEEE Workshop on Spontanous Speech Processing and Recognition, Tokyo, Japan, pp. 119–125 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sun, R., Moore, E. (2011). Investigating Acoustic Cues in Automatic Detection of Learners’ Emotion from Auto Tutor. In: D’Mello, S., Graesser, A., Schuller, B., Martin, JC. (eds) Affective Computing and Intelligent Interaction. ACII 2011. Lecture Notes in Computer Science, vol 6975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24571-8_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-24571-8_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24570-1
Online ISBN: 978-3-642-24571-8
eBook Packages: Computer ScienceComputer Science (R0)