1 / 39

Deceptive Speech

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.

Download Presentation

Deceptive Speech

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Deceptive Speech Frank Enos • April 19, 2006

  2. 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

  3. Why study deception? • Law enforcement / Jurisprudence • Intelligence / Military / Security • Business • Politics • Mental health practitioners • Social situations • Is it ever good to lie?

  4. 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

  5. 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

  6. People Are Terrible At This

  7. 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

  8. 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

  9. Frank Tells Some Lies An Example…

  10. 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!

  11. How to Lie (Ekman‘’01) • Concealment • Falsification • Misdirecting • Telling the truth falsely • Half-concealment • Incorrect inference dodge.

  12. • 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!

  13. Reasons To Lie (Frank‘’92 ) • Self-preservation • Self-presentation • *Gain • Altruistic (social) lies

  14. 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

  15. 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

  16. 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

  17. Features of Deception • Cognitive • Coherence, fluency • Interpersonal • Discourse features: DA, turn-taking, etc. • Emotion

  18. 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)

  19. 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

  20. 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

  21. Modeling Deception in Speech • Lexical • Prosodic/Acoustic • Discourse

  22. Deception in Speech (Depaulo ’03) • Positive Correlates • Interrupted/repeated words • References to “external” events • Verbal/vocal uncertainty • Vocal tension • F0

  23. Deception in Speech (Depaulo ’03) • Negative Correlates • Subject stays on topic • Admitted uncertainties • Verbal/vocal immediacy • Admitted lack of memory • Spontaneous corrections

  24. Problems, revisited • Differences due to: • Gender • Social Status • Language • Culture • Personality

  25. 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

  26. 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”

  27. 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

  28. 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)

  29. 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

  30. 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

  31. 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)

  32. 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.

  33. By Judge 58.2% Acc. By Interviewee

  34. Personality Measure: NEO-FFI • Costa & McCrae (1992) Five-factor model • Openness to Experience • Conscientiousness • Extraversion • Agreeability • Neuroticism • Widely used in psychology literature

  35. Neuroticism, Openness & Agreeableness correlate with judge performance WRT Global lies.

  36. These factors also provide strongly predictive models for accuracy at global lies.

  37. 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)

  38. 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

More Related