Name
Title | ||
|---|---|---|
Causal indicators for assessing the truthfulness of child speech in forensic interviews |
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 indicators of truth-telling and that the coordination of child and interviewer speech signals is much more informative than the specific language used throughout the interview. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.csl.2021.101263 | COMPUTER SPEECH AND LANGUAGE |
Keywords | DocType | Volume |
Automated deception detection, Narrative truth induction, Child forensic interviewing, Granger causal analysis | Journal | 71 |
ISSN | Citations | PageRank |
0885-2308 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zane Durante | 1 | 0 | 0.34 |
| Victor Ardulov | 2 | 0 | 1.69 |
| Manoj Kumar | 3 | 31 | 20.96 |
| Jennifer Gongola | 4 | 0 | 0.34 |
| Thomas D Lyon | 5 | 1 | 1.71 |
| Narayanan Shrikanth | 6 | 5558 | 439.23 |