Automatic deception detection is an important problem with far-reaching implications for many disciplines. We present a series of experiments aimed at automatically detecting deception from speech. We use the Columbia X-Cultural Deception (CXD) Corpus, a large-scale corpus of within-subject deceptive and non-deceptive speech, for training and evaluating our models. We compare the use of spectral, acoustic-prosodic, and lexical feature sets, using different machine learning models. Finally, we design a single hybrid deep model with both acoustic and lexical features trained jointly that achieves state-of-the-art results on the CXD corpus.
Cite as: Mendels, G., Levitan, S.I., Lee, K.-Z., Hirschberg, J. (2017) Hybrid Acoustic-Lexical Deep Learning Approach for Deception Detection. Proc. Interspeech 2017, 1472-1476, doi: 10.21437/Interspeech.2017-1723
@inproceedings{mendels17_interspeech,
author={Gideon Mendels and Sarah Ita Levitan and Kai-Zhan Lee and Julia Hirschberg},
title={{Hybrid Acoustic-Lexical Deep Learning Approach for Deception Detection}},
year=2017,
booktitle={Proc. Interspeech 2017},
pages={1472--1476},
doi={10.21437/Interspeech.2017-1723}
}