CSC Deceptive Speech was developed by Columbia University, SRI International
and University of Colorado Boulder. It consists of 32 hours of audio interview
from 32 native speakers of Standard American English
(16 male,16 female) recruited from the Columbia University student population
and the community. The purpose of the study was to distinguish deceptive speech
from non-deceptive speech using machine learning techniques on extracted features
from the corpus.
The participants were told that they were participating in a communication
experiment which sought to identify people who fit the profile of the top entrepreneurs
in America. To this end, the participants performed tasks and answered questions
in six areas. They were later told that they had received low scores in some
of those areas and did not fit the profile. The subjects then participated in
an interview where they were told to convince the interviewer that they had
actually achieved high scores in all areas and that they did indeed fit the
profile. The task of the interviewer was to determine how he thought the subjects
had actually performed, and he was allowed to ask them any questions other than
those that were part of the performed tasks. For each question from the interviewer,
subjects were asked to indicate whether the reply was true or contained any
false information by pressing one of two pedals hidden from the interviewer
under a table.
Interviews were conducted in a double-walled sound booth and recorded to digital
audio tape on two channels using Crown CM311A Differoid headworn close-talking
microphones, then downsampled to 16kHz before processing.
The interviews were orthographically transcribed by hand using the NIST EARS
transcription guidelines. Labels for local lies were obtained automatically
from the pedal-press data and hand-corrected for alignment, and labels for global
lies were annotated during transcription based on the known scores of the subjects
versus their reported scores. The orthographic transcription was force-aligned
using the SRI telephone speech recognizer adapted for full-bandwidth recordings.
There are several segmentations associated with the corpus: the implicit segmentation
of the pedal presses, derived semi-automatically sentence-like units (EARS
SLASH-UNITS or SUs) which were hand labeled, intonational phrase units and
the units corresponding to each topic of the interview.
Transcript files are in .trs format and audio files
are .wav presented in flac-compressed form
for this release.
Please view these audio
and transcript samples for the interviewer side of a conversation..
None at this time.
Portions © 2013 The Trustees of Columbia University, Trustees of the University