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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.

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deceptive speech

Deceptive Speech

Frank Enos • April 19, 2006

defining deception
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
Why study deception?
  • Law enforcement / Jurisprudence
  • Intelligence / Military / Security
  • Business
  • Politics
  • Mental health practitioners
  • Social situations
    • Is it ever good to lie?
why study deception4
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
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
problems in studying deception7
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
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 lies10
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
How to Lie (Ekman‘’01)
  • Concealment
  • Falsification
  • Misdirecting
  • Telling the truth falsely
  • Half-concealment
  • Incorrect inference dodge.
frank tells some lies12

• 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!

reasons to lie frank 92
Reasons To Lie (Frank‘’92 )
  • Self-preservation
  • Self-presentation
  • *Gain
  • Altruistic (social) lies
how not to lie ekman 01
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 0115
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 0116
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
Features of Deception
  • Cognitive
    • Coherence, fluency
  • Interpersonal
    • Discourse features: DA, turn-taking, etc.
  • Emotion
describing 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
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
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
Modeling Deception in Speech
  • Lexical
  • Prosodic/Acoustic
  • Discourse
deception in speech depaulo 03
Deception in Speech (Depaulo ’03)
  • Positive Correlates
    • Interrupted/repeated words
    • References to “external” events
    • Verbal/vocal uncertainty
    • Vocal tension
    • F0
deception in speech depaulo 0323
Deception in Speech (Depaulo ’03)
  • Negative Correlates
    • Subject stays on topic
    • Admitted uncertainties
    • Verbal/vocal immediacy
    • Admitted lack of memory
    • Spontaneous corrections
problems revisited
Problems, revisited
  • Differences due to:
    • Gender
    • Social Status
    • Language
    • Culture
    • Personality
columbia sri colorado corpus
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 corpus26
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 corpus27
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
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)
slide30

Columbia University– SRI/ICSI – University of Colorado Deception Corpus: An Example Segment

SEGMENT TYPE

Breath Group

LABEL

LIE

Obtained

from subject

pedal presses.

um i was visiting a friend in venezuela and we went camping

ACOUSTIC FEATURES

max_corrected_pitch5.7

mean_corrected_pitch5.3

pitch_change_1st_word -6.7

pitch_change_last_word-11.5

normalized_mean_energy0.2

unintelligible_words 0.0

Produced

automatically

using lexical

transcription.

Produced using

ASR output

and other

acoustic analyses

LEXICAL FEATURES

has_filled_pauseYES

positive_emotion_wordYES

uses_past_tense NO

negative_emotion_wordNO

contains_pronoun_iYES

verbs_in_gerund YES

PREDICTION

LIE

csc corpus results
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
slide32

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
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.
slide34

By Judge

58.2% Acc.

By Interviewee

personality measure neo ffi
Personality Measure: NEO-FFI
  • Costa & McCrae (1992) Five-factor model
    • Openness to Experience
    • Conscientiousness
    • Extraversion
    • Agreeability
    • Neuroticism
  • Widely used in psychology literature
other perception findings
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
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