When a child is suspected to be the victim or sole witness of a crime, the manner in which information is gathered from the child becomes critical. A child forensic interview is the guided conversation that a legal expert conducts to elicit reliable information from a child. To help substantiate child testimony, it is important to discern characteristics of truthful and deceptive behavior in these interviews. The work presented uses various machine learning algorithms to identify differences in the speech of children when they are lying or being truthful, particularly when they have been asked by a confederate to deceive an interviewer. Results show that vocabulary and psycho-linguistic norms of a child's language use, in response to directed questions, provide substantial information to outperform human adults in detecting truthful statements.