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Deceptive Speech
Frank Enos • April 19, 2006
Defining Deception
Deliberate choice to mislead a target
without prior notification (Ekman‘’01)
Often to gain some advantage
Excludes: Self-deception Theater, etc. Falsehoods due to ignorance/error Pathological behaviors
Why study deception?
Law enforcement / Jurisprudence
Intelligence / Military / Security
Business
Politics
Mental health practitioners
Social situations Is it ever good to lie?
Why study deception?
What makes speech “believable”?
Recognizing deception means recognizing
intention.
How do people spot a liar?
How does this relate to other subjective
phenomena in speech? E.g. emotion,
charisma
Problems in studying deception?
Most people are terrible at detecting deception
— ~50% accuracy
(Ekman & O’sullivan 1991, Aamodt 2006, etc.)
People use subjective judgments —
emotion, etc.
Recognizing emotion is hard
People Are Terrible At This
Group #Studies #Subjects Accuracy %
Criminals 1 52 65.40
Secret service 1 34 64.12
Psychologists 4 508 61.56
Judges 2 194 59.01
Cops 8 511 55.16
Federal officers 4 341 54.54
Students 122 8,876 54.20
Detectives 5 341 51.16
Parole officers 1 32 40.42
Problems in studying deception?
Hard to get good data Real world (example) Laboratory
Ethical issues Privacy Subject rights Claims of success
But also ethical imperatives: Need for reliable methods Debunking faulty methods False confessions
20th Century Lie Detection
Polygraph http://antipolygraph.org
The Polygraph and Lie Detection (N.A.P. 2003)
Voice Stress Analysis Microtremors 8-12Hz Universal Lie response http://www.love-detector.com/ http://news-info.wustl.edu/news/page/normal/669.html
Reid Behavioral Analysis Interview Interrogation
Frank Tells Some LiesAn Example…
Frank Tells Some Lies
Maria: I’m buying tickets to Händel’s Messiah for me
and my friends — would you like to join us?
Frank: When is it?
Maria: December 19th.
Frank: Uh… the 19th…
Maria: My two friends from school are coming, and
Robin…
Frank: I’d love to!
How to Lie (Ekman‘’01)
Concealment
Falsification
Misdirecting
Telling the truth falsely
Half-concealment
Incorrect inference dodge.
Frank Tells Some Lies
Maria: I’m buying tickets to Handel’s Messiah for me
and my friends — would you like to join us?
Frank: When is it?
Maria: December 19th.
Frank: Uh… the 19th…
Maria: My two friends from school
are coming, and Robin…
Frank: I’d love to!
• Concealment
• Falsification
• Misdirecting
• Telling the truth falsely
• Half-concealment
• Incorrect inference dodge.
Reasons To Lie (Frank‘’92 )
Self-preservation
Self-presentation
*Gain
Altruistic (social) lies
How Not To Lie (Ekman‘’01)
Leakage Part of the truth comes out Liar shows inconsistent emotion Liar says something inconsistent with the lie
Deception clues Indications that the speaker is deceiving Again, can be emotion Inconsistent story
How Not To Lie (Ekman‘’01)
Bad lines Lying well is hard Fabrication means keeping story straight Concealment means remembering what is omitted All this creates cognitive load harder to hide emotion
Detection apprehension (fear) Target is hard to fool Target is suspicious Stakes are high Serious rewards and/or punishments are at stake Punishment for being caught is great
How Not To Lie (Ekman‘’01)
Deception guilt Stakes for the target are high Deceit is unauthorized Liar is not practiced at lying Liar and target are acquainted Target can’t be faulted as mean or gullible Deception is unexpected by target
Duping delight Target poses particular challenge Lie is a particular challenge Others can appreciate liar’s performance
Features of Deception
Cognitive Coherence, fluency
Interpersonal Discourse features: DA, turn-taking, etc.
Emotion
Describing Emotion
Primary emotions Acceptance, anger, anticipation, disgust, joy,
fear, sadness, surprise
One approach:
continuous dim. model (Cowie/Lang)
Activation – evaluation space
Add control/agency
Primary E’s differ on at least 2 dimensions of this
scale (Pereira)
Problems With Emotion and Deception
Relevant emotions may not differ much on
these scales
Othello error People are afraid of the police People are angry when wrongly accused People think pizza is funny
Brokow hazard Failure to account for individual differences
Bulk of extant deception research…
Not focused on verifying 20th century
techniques
Done by psychologists
Considers primarily facial and physical cues
“Speech is hard”
Little focus on automatic detection of
deception
Modeling Deception in Speech
Lexical
Prosodic/Acoustic
Discourse
Deception in Speech (Depaulo ’03)
Positive Correlates Interrupted/repeated words References to “external” events Verbal/vocal uncertainty Vocal tension F0
Deception in Speech (Depaulo ’03)
Negative Correlates Subject stays on topic Admitted uncertainties Verbal/vocal immediacy Admitted lack of memory Spontaneous corrections
Problems, revisited
Differences due to: Gender Social Status Language Culture Personality
Columbia/SRI/Colorado Corpus
With Julia Hirschberg, Stefan Benus, and colleagues from SRI/ICSI and U. C. Boulder
Goals Examine feasibility of automatic deception
detection using speech Discover or verify acoustic/prosodic, lexical,
and discourse correlates of deception Model a “non-guilt” scenario Create a “clean” corpus
Columbia/SRI/Colorado Corpus
Inflated-performance scenario
Motivation: financial gain
and self-presentation
32 Subjects: 16 women, 16 men
Native speakers of Standard American English
Subjects told study seeks to identify people who
match profile based on “25 Top Entrepreneurs”
Columbia/SRI/Colorado Corpus
Subjects take test in six categories: Interactive, music, survival, food,
NYC geography, civics
Questions manipulated 2 too high; 2 too low; 2 match
Subjects told study also seeks people who can convince interviewer they match profile Self-presentation + reward
Subjects undergo recorded interview in booth Indicate veracity of factual content of each utterance using
pedals
CSC Corpus: Data
15.2 hrs. of interviews; 7 hrs subject speech
Lexically transcribed & automatically aligned
lexical/discourse features
Lie conditions: Global Lie / Local Lie
Segmentations (LT/LL):
slash units (5709/3782), phrases
(11,612/7108), turns (2230/1573)
Acoustic features (± recognizer output)
um i was visiting a friend in venezuela and we went camping
Columbia University– SRI/ICSI – University of Colorado Deception Corpus: An Example Segment
Breath GroupSEGMENT TYPE
LABEL
ACOUSTIC FEATURES
LEXICAL FEATURES
LIE
max_corrected_pitch 5.7mean_corrected_pitch 5.3pitch_change_1st_word -6.7
pitch_change_last_word -11.5normalized_mean_energy 0.2unintelligible_words 0.0
Obtainedfrom subject
pedal presses.
has_filled_pause YESpositive_emotion_word YESuses_past_tense NO
negative_emotion_word NOcontains_pronoun_i YES verbs_in_gerund YES
Produced usingASR output
and otheracoustic analyses
Produced automaticallyusing lexicaltranscription.
LIEPREDICTION
CSC Corpus: Results
Classification (Ripper rule induction, randomized 5-fold cv) Slash Units / Local Lies — Baseline 60.2%
Lexical & acoustic: 62.8 %; + subject dependent: 66.4% Phrases / Local Lies — Baseline 59.9%
Lexical & acoustic 61.1%; + subject dependent: 67.1%
Other findings Positive emotion words deception (LIWC) Pleasantness deception (DAL) Filled pauses truth Some pitch correlation — varies with subject
Example JRIP rules:
(cueLieToCueTruths >= 2) and (TOPIC = topic_newyork) and (numSUwithFPtoNumSU <= 0) and (wu_ENERGY_NO_UV_STY_MAX__EG_ZNORM-ENERGY_NO_UV_STY_MIN__EG_ZNORM-D <= 5.846) => PEDAL=L (231.0/61.0)
(cueLieToCueTruths >= 2) and (numSUwithFPtoNumSU <= 1) and (wu_ENERGY_NO_UV_STY_MAX__EG_ZNORM-ENERGY_NO_UV_STY_MIN__EG_ZNORM-D <= 5.68314) and (wu_ENERGY_NO_UV_RAW_MAX-ENERGY_NO_UV_RAW_MIN-D >= 8.41605) and (wu_F0_SLOPES_NOHD__LAST >= -2.004) => PEDAL=L (284.0/117.0)
(cueLieToCueTruths >= 2) and (wu_F0_RAW_MAX >= 5.706379) and (wu_DUR_PHONE_SPNN_AV <= 1.0661) => PEDAL=L (262.0/115.0)
CSC Corpus: A Perception Study
With Julia Hirschberg, Stefan Benus, Robin Cautin and colleagues from SRI/ICSI
32 Judges Each judge rated 2 interviews Judge Labels:
Local Lie using Praat Global Lie on paper
Takes pre- and post-test questionnaires Personality Inventory Judge receives ‘training’ on one subject.
By Judge
58.2% Acc.
By Interviewee
Personality Measure: NEO-FFI
Costa & McCrae (1992) Five-factor model Openness to Experience Conscientiousness Extraversion Agreeability Neuroticism
Widely used in psychology literature
Neuroticism, Openness & Agreeableness correlate with judge performance
WRT Global lies.
These factors also provide
strongly predictive
models for accuracy at global lies.
Other Perception Findings
No effect for training Judges’ post-test confidence did not correlate
with pre-test confidence Judges who claimed experience had
significantly higher pre-test confidence But not higher accuracy!
Many subjects used disfluencies as cues to D. In this corpus, disfluencies correlate with TRUTH!
(Benus et al. ‘06)
Our Future Work
Individual differences Wizards of deception
Predicting Global Lies Local lies as ‘hotspots’
New paradigm Shorter Addition of personality test for speakers Addition of cognitive load