Durante Zane, Ardulov Victor, Kumar Manoj, Gongola Jennifer, Lyon Thomas, Narayanan Shrikanth
University of Southern California Los Angeles, CA, USA.
Comput Speech Lang. 2022 Jan;71. doi: 10.1016/j.csl.2021.101263. Epub 2021 Jul 19.
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.
在询问一个可能目睹犯罪的儿童时,采访者必须提出经过精心设计的问题,以便从儿童那里得到真实的陈述。本文运用格兰杰因果分析来研究和呈现此类采访中儿童与采访者之间的互动动态。我们的研究表明,对言语中的心理语言学和声学信号进行格兰杰因果分析,能够显著预测儿童是否在说实话,以及儿童是否会在采访后期透露目睹过违法行为。通过纳入从法医访谈的音频和文字记录中提取的跨模态格兰杰因果特征,我们能够大大超越传统的欺骗检测方法和一些模拟基线。我们的结果表明,儿童在语言中对具体性和形象性的运用是如实陈述的强有力的心理语言学指标,而且儿童与采访者言语信号的协调性比整个采访中使用的具体语言更具信息量。