Paper Info
Title
Learning backchannel prediction model from parasocial consensus sampling: a subjective evaluation
Abstract
Backchannel feedback is an important kind of nonverbal feedback within face-to-face interaction that signals a person's interest, attention and willingness to keep listening. Learning to predict when to give such feedback is one of the keys to creating natural and realistic virtual humans. Prediction models are traditionally learned from large corpora of annotated face-to-face interactions, but this approach has several limitations. Previously, we proposed a novel data collection method, Parasocial Consensus Sampling, which addresses these limitations. In this paper, we show that data collected in this manner can produce effective learned models. A subjective evaluation shows that the virtual human driven by the resulting probabilistic model significantly outperforms a previously published rule-based agent in terms of rapport, perceived accuracy and naturalness, and it is even better than the virtual human driven by real listeners' behavior in some cases.
Year
DOI
Venue
2010
10.1007/978-3-642-15892-6_17
IVA
Keywords
Field
DocType
nonverbal feedback,important kind,parasocial consensus sampling,annotated face-to-face interaction,realistic virtual human,subjective evaluation,novel data collection method,backchannel prediction model,face-to-face interaction,prediction model,large corpus,backchannel feedback,data collection,probabilistic model,virtual human,rule based
Data collection,Computer science,Naturalness,Active listening,Nonverbal communication,Statistical model,Artificial intelligence,Virtual actor,Parasocial interaction,Machine learning,Backchannel
Conference
Volume
ISSN
ISBN
6356
0302-9743
3-642-15891-9
Citations 
PageRank 
References 
15
0.97
16
Authors
3
Name
Order
Citations
PageRank
Lixing Huang11138.89
Louis-Philippe Morency23220200.79
Jonathan Gratch33721379.33