Abstract
Researchers in both psychology and computer science have suggested that modeling individual differences may improve the performance of automatic deception detection systems. In this study, we fuse a personality classification task with a deception classifier and evaluate various ways to combine the two tasks, either as a single network with shared layers, or by feeding personality labels into the deception classifier. We show that including personality recognition improves the performance of deception detection on the Columbia X-Cultural Deception (CXD) corpus by more than 6% relative, achieving new state-of-the-art results on classification of phrase-like units in this corpus.
| Original language | English |
|---|---|
| Pages (from-to) | 421-425 |
| Number of pages | 5 |
| Journal | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
| Volume | 2018-September |
| DOIs | |
| State | Published - 2018 |
| Externally published | Yes |
| Event | 19th Annual Conference of the International Speech Communication, INTERSPEECH 2018 - Hyderabad, India Duration: 2 Sep 2018 → 6 Sep 2018 |
Bibliographical note
Publisher Copyright:© 2018 International Speech Communication Association. All rights reserved.
Keywords
- Deception detection
- DNN
- LSTM
- Personality recognition
- Word Embedding