Title DECEPTION DETECTION

Published Santa Monica, CA : RAND, 2022

Click on the following:
JSTOR Security

Copies

Description 1 online resource (3 pages) : illustrations Summary In this report, the authors developed and tested the use of machine-learning methods to detect speech patterns that reflect attempts at deception or truthfulness during simulated security clearance background interviews Related To See also RAND/RR-A873-1 nnam Looking for Lies: An Exploratory Analysis for Automated Detection of Deception RAND/RR-A873-1 Notes Title from PDF document (viewed October 19, 2022) Bibliography Includes bibliographical references Notes Description based on electronic resource Form Electronic book Author Posard, Marek Johnson, Christian Melin, Julia L Ellinger, Emily Reininger, Hilary National Defense Research Institute (U.S.). Forces and Resources Policy Center. Rand Corporation.

  Permalink