Title
Learning multimodal behavioral models for face-to-face social interaction
Abstract
The aim of this paper is to model multimodal perception-action loops of human behavior in face-to-face interactions. To this end, we propose trainable behavioral models that predict the optimal actions for one specific person given others’ perceived actions and the joint goals of the interlocutors. We first compare sequential models—in particular discrete hidden Markov models (DHMMs)—with standard classifiers (SVMs and decision trees). We propose a modification of the initialization of the DHMMs in order to better capture the recurrent structure of the sensory-motor states. We show that the explicit state duration modeling by discrete hidden semi markov models (DHSMMs) improves prediction performance. We applied these models to parallel speech and gaze data collected from interacting dyads. The challenge was to predict the gaze of one subject given the gaze of the interlocutor and the voice activity of both. For both DHMMs and DHSMMs the short-time Viterbi concept is used for incremental decoding and prediction. For the proposed models we evaluated objectively several properties in order to go beyond pure classification performance. Results show that incremental DHMMs (IDHMMs) were more efficient than classic classifiers and superseded by incremental DHSMMs (IDHSMMs). This later result emphasizes the relevance of state duration modeling.
Year
DOI
Venue
2015
10.1007/s12193-015-0190-7
J. Multimodal User Interfaces
Keywords
Field
DocType
Sensory-motor behavior,Interaction unit recognition,Gaze prediction,Hidden Semi-Markov Model
Decision tree,Gaze,Markov model,Computer science,Support vector machine,Artificial intelligence,Initialization,Hidden Markov model,Machine learning,Viterbi algorithm,Hidden semi-Markov model
Journal
Volume
Issue
ISSN
9
3
1783-7677
Citations 
PageRank 
References 
7
0.51
44
Authors
4
Name
Order
Citations
PageRank
Alaeddine Mihoub1242.86
Gérard Bailly260999.37
Christian Wolf3102754.93
Frédéric Elisei427525.05