Abstract

When a child is suspected to be the victim or sole witness of a crime, the manner in which information is gathered from the child becomes critical. A child forensic interview is the guided conversation that a legal expert conducts to elicit reliable information from a child. To help substantiate child testimony, it is important to discern characteristics of truthful and deceptive behavior in these interviews. The work presented uses various machine learning algorithms to identify differences in the speech of children when they are lying or being truthful, particularly when they have been asked by a confederate to deceive an interviewer. Results show that vocabulary and psycho-linguistic norms of a child's language use, in response to directed questions, provide substantial information to outperform human adults in detecting truthful statements.

Date

2020

Authors

Victor Ardulov, Zane Durante, Shanna Williams, Thomas Lyon, Shrikanth Narayanan

Conference

ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Pages

8074-8078

Publisher

IEEE