Abstract

When interviewing a child who may have witnessed a crime, the interviewer must ask carefully directed questions in order to elicit a truthful statement from the child. The presented work uses Granger causal analysis to examine and represent child–interviewer interaction dynamics over such an interview. Our work demonstrates that Granger Causal analysis of psycholinguistic and acoustic signals from speech yields significant predictors of whether a child is telling the truth, as well as whether a child will disclose witnessing a transgression later in the interview. By incorporating cross-modal Granger causal features extracted from audio and transcripts of forensic interviews, we are able to substantially outperform conventional deception detection methods and a number of simulated baselines. Our results suggest that a child’s use of concreteness and imageability in their language are strong psycholinguistic …

Date

2022

Authors

Zane Durante, Victor Ardulov, Manoj Kumar, Jennifer Gongola, Thomas Lyon, Shrikanth Narayanan

Journal

Computer speech & language

Volume

71

Pages

101263

Publisher

Academic Press