Zane Durante, Victor Ardulov, Manoj Kumar, Jennifer Gongola, Thomas Lyon, and Shrikanth Narayanan. Causal indicators for assessing the truthfulness of child speech in forensic interviews. Computer Speech & Language, 71:101263, 2022.

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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.

BibTeX Entry

@article{DURANTE2022101263,
title = {Causal indicators for assessing the truthfulness of child speech in forensic interviews},
journal = {Computer Speech & Language},
volume = {71},
pages = {101263},
year = {2022},
issn = {0885-2308},
doi = {https://doi.org/10.1016/j.csl.2021.101263},
url = {https://www.sciencedirect.com/science/article/pii/S0885230821000693},
link = {http://sail.usc.edu/publications/files/Durante-CSL2022.pdf},
author = {Zane Durante and Victor Ardulov and Manoj Kumar and Jennifer Gongola and Thomas Lyon and Shrikanth Narayanan},
keywords = {Automated deception detection, Narrative truth induction, Child forensic interviewing, Granger causal analysis},
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.}
}

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