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Prosody-based automatic detection of annoyance and frustration in human-computer dialog

Ang, Jeremy, Dhillon, Rajdip, Krupski, Ashley, Shriberg, Elizabeth, Stolcke, Andreas·2002·7th International Conference on Spoken Language Processing (ICSLP 2002)

Abstract

We investigate the use of prosody for the detection of frustration and annoyance in natural human-computer dialog. In addition to prosodic features, we examine the contribution of language model information and speaking "style". Results show that a prosodic model can predict whether an utterance is neutral versus "annoyed or frustrated" with an accuracy on par with that of human interlabeler agreement. Accuracy increases when discriminating only "frustrated" from other utterances, and when using only those utterances on which labelers originally agreed. Furthermore, prosodic model accuracy degrades only slightly when using recognized versus true words. Language model features, even if based on true words, are relatively poor predictors of frustration. Finally, we find that hyperarticulation is not a good predictor of emotion; the two phenomena often occur independently.

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Metadata

Authors
Ang, J., Dhillon, R., Krupski, A., Shriberg, E., & Stolcke, A.
Venue
7th International Conference on Spoken Language Processing (ICSLP 2002)
Publisher
ISCA
Published
2002-09-16
Pages
2037-2040
OpenAlex
W1514944708
Cited by
386
References
0
FWCI
32.17
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Prosody-based automatic detection of annoyance and frustration in human-computer dialog. https://app.cochat.ai/papers/10.21437/icslp.2002-559
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Ang, J., Dhillon, R., Krupski, A., Shriberg, E., & Stolcke, A. (2002). Prosody-based automatic detection of annoyance and frustration in human-computer dialog. 7th International Conference on Spoken Language Processing (ICSLP 2002). https://doi.org/10.21437/icslp.2002-559

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.