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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 l.jpg

Deceptive Speech

Frank Enos • April 19, 2006


Defining deception l.jpg
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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Why study deception?

  • Law enforcement / Jurisprudence

  • Intelligence / Military / Security

  • Business

  • Politics

  • Mental health practitioners

  • Social situations

    • Is it ever good to lie?


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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


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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



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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


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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


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Frank Tells Some Lies

An Example…


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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!


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How to Lie (Ekman‘’01)

  • Concealment

  • Falsification

  • Misdirecting

  • Telling the truth falsely

  • Half-concealment

  • Incorrect inference dodge.


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  • • 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!


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Reasons To Lie (Frank‘’92 )

  • Self-preservation

  • Self-presentation

  • *Gain

  • Altruistic (social) lies


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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


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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


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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


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Features of Deception

  • Cognitive

    • Coherence, fluency

  • Interpersonal

    • Discourse features: DA, turn-taking, etc.

  • Emotion


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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)


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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


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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


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Modeling Deception in Speech

  • Lexical

  • Prosodic/Acoustic

  • Discourse


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Deception in Speech (Depaulo ’03)

  • Positive Correlates

    • Interrupted/repeated words

    • References to “external” events

    • Verbal/vocal uncertainty

    • Vocal tension

    • F0


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Deception in Speech (Depaulo ’03)

  • Negative Correlates

    • Subject stays on topic

    • Admitted uncertainties

    • Verbal/vocal immediacy

    • Admitted lack of memory

    • Spontaneous corrections


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Problems, revisited

  • Differences due to:

    • Gender

    • Social Status

    • Language

    • Culture

    • Personality


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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


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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”


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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


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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)


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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


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CSC Corpus: Results Deception Corpus: An Example Segment

  • 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


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Example JRIP rules: Deception Corpus: An Example Segment

(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)


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CSC Corpus: A Perception Study Deception Corpus: An Example Segment

  • 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.


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By Judge Deception Corpus: An Example Segment

58.2% Acc.

By Interviewee


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Personality Measure: NEO-FFI Deception Corpus: An Example Segment

  • Costa & McCrae (1992) Five-factor model

    • Openness to Experience

    • Conscientiousness

    • Extraversion

    • Agreeability

    • Neuroticism

  • Widely used in psychology literature


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Neuroticism, Openness & Agreeableness Deception Corpus: An Example Segmentcorrelate with judge performance

WRT Global lies.



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Other Perception Findings accuracy at global lies.

  • 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)


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Our Future Work accuracy at global lies.

  • 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


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