Papers by Reynold Bailey

2 papers
Multimodal Modeling of Task-Mediated Confusion (2022.naacl-srw)

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Challenge: Existing models for confusion detection are under-explored, but they can be used to detect it computationally.
Approach: They build upon prior work to develop models that detect confusion from three modalities: video, audio, and text.
Outcome: The proposed models can detect confusion from facial expressions, prosody, and transcribed spoken language.
MULTICOLLAB: A Multimodal Corpus of Dialogues for Analyzing Collaboration and Frustration in Language (2024.lrec-main)

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Challenge: Existing methods to study complex emotions when a speaker collaborates with a partner are limited.
Approach: They propose to fuse a multimodal dialogue resource with transcribed speech and eye gaze data to create a highly multimodal corpus.
Outcome: The proposed model improves classification accuracy by 21% over baseline using sensor and speech data in 4.5 seconds.

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