Paper: Automatic detection of deception in child-produced speech using syntactic complexity features

ACL ID P13-1093
Title Automatic detection of deception in child-produced speech using syntactic complexity features
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

It is important that the testimony of chil- dren be admissible in court, especially given allegations of abuse. Unfortunately, children can be misled by interrogators or might offer false information, with dire consequences. In this work, we evalu- ate various parameterizations of five clas- sifiers (including support vector machines, neural networks, and random forests) in deciphering truth from lies given tran- scripts of interviews with 198 victims of abuse between the ages of 4 and 7. These evaluations are performed using a novel set of syntactic features, including mea- sures of complexity. Our results show that sentence length, the mean number of clauses per utterance, and the Stajner- Mitkov measure of complexity are highly informative syntactic features, that classi- fication accur...