Hybrid Acoustic-Lexical Deep Learning Approach for Deception Detection

Gideon Mendels, Sarah Ita Levitan, Kai-Zhan Lee, Julia Hirschberg


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.


 DOI: 10.21437/Interspeech.2017-1723

Cite as: Mendels, G., Levitan, S.I., Lee, K., Hirschberg, J. (2017) Hybrid Acoustic-Lexical Deep Learning Approach for Deception Detection. Proc. Interspeech 2017, 1472-1476, DOI: 10.21437/Interspeech.2017-1723.


@inproceedings{Mendels2017,
  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},
  url={http://dx.doi.org/10.21437/Interspeech.2017-1723}
}