Conversational Engagement Recognition Using Auditory and Visual Cues

Yuyun Huang, Emer Gilmartin, Nick Campbell


Automatic prediction of engagement in human-human and human-machine dyadic and multiparty interaction scenarios could greatly aid in evaluation of the success of communication. A corpus of eight face-to-face dyadic casual conversations was recorded and used as the basis for an engagement study, which examined the effectiveness of several methods of engagement level recognition. A convolutional neural network based analysis was seen to be the most effective.


DOI: 10.21437/Interspeech.2016-846

Cite as

Huang, Y., Gilmartin, E., Campbell, N. (2016) Conversational Engagement Recognition Using Auditory and Visual Cues. Proc. Interspeech 2016, 590-594.

Bibtex
@inproceedings{Huang+2016,
author={Yuyun Huang and Emer Gilmartin and Nick Campbell},
title={Conversational Engagement Recognition Using Auditory and Visual Cues},
year=2016,
booktitle={Interspeech 2016},
doi={10.21437/Interspeech.2016-846},
url={http://dx.doi.org/10.21437/Interspeech.2016-846},
pages={590--594}
}