Transfer learning (TL) involves leveraging information from sources outside the domain at hand for enhancing model performances. Popular TL methods either directly use the data or adapt the models learned on out-of-domain resources and incorporate them within in-domain models. TL methods have shown promise in several applications such as text classification, cross-domain language classification and emotion recognition. In this paper, we propose TL methods to computational human behavioral trait modeling. Many behavioral traits are abstract constructs (e.g., sincerity of an individual), and are often conceptually related to other constructs (e.g., level of deception) making TL methods an attractive option for their modeling. We consider the problem of automatically predicting human sincerity and deception from behavioral data while leveraging transfer of knowledge from each other. We compare our methods against baseline models trained only on in-domain data. Our best models achieve an Unweighted Average Recall (UAR) of 72.02% in classifying deception (baseline: 69.64%). Similarly, applied methods achieve Spearman’s/Pearson’s correlation values of 49.37%/48.52% between true and predicted sincerity scores (baseline: 46.51%/41.58%), indicating the success and the potential of TL for such human behavior tasks.
Cite as: Luo, Q., Gupta, R., Narayanan, S.S. (2017) Transfer Learning Between Concepts for Human Behavior Modeling: An Application to Sincerity and Deception Prediction. Proc. Interspeech 2017, 1462-1466, doi: 10.21437/Interspeech.2017-121
@inproceedings{luo17_interspeech,
author={Qinyi Luo and Rahul Gupta and Shrikanth S. Narayanan},
title={{Transfer Learning Between Concepts for Human Behavior Modeling: An Application to Sincerity and Deception Prediction}},
year=2017,
booktitle={Proc. Interspeech 2017},
pages={1462--1466},
doi={10.21437/Interspeech.2017-121}
}