1 Deceptive Speech CS4706 Julia Hirschberg 2 Everyday Lies Ordinary people tell an average of 2 lies...

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Transcript of 1 Deceptive Speech CS4706 Julia Hirschberg 2 Everyday Lies Ordinary people tell an average of 2 lies...

  • Deceptive Speech

    CS4706Julia Hirschberg

  • Everyday LiesOrdinary people tell an average of 2 lies per dayYour new hair-cut looks great.Im sorry I missed your talk but .In many cultures white lies are more acceptable than the truthLikelihood of being caught is lowRewards also low but outweigh consequences of being caughtNot so easy to detect

  • Serious LiesLies whereRisks highRewards highEmotional consequences more apparentAre these lies easier to detect?By humans?By machines?

  • OutlineResearch on DeceptionPossible cues to deceptionCurrent approachesOur corpus-based study of deceptive speechApproachCorpus collection/paradigmFeatures extractedExperiments and resultsHuman perception studiesFuture research

  • A Definition of DeceptionDeliberate choice to misleadWithout prior notificationTo gain some advantage or to avoid some penaltyNot:Self-deception, delusion, pathological behaviorTheaterFalsehoods due to ignorance/error

  • Who Studies Deception?Students of human behavior especially psychologistsLaw enforcement and military personnelCorporate security officersSocial services workers Mental health professionals

  • Why is Lying Difficult for Most of Us?Hypotheses: Our cognitive load is increased when lying becauseMust keep story straightMust remember what weve said and what we havent saidOur fear of detection is increased ifWe believe our target is hard to foolWe believe our target is suspiciousStakes are high: serious rewards and/or punishmentsMakes it hard for us to control indicators of deceptionDoes this make deception easy to detect?

  • Cues to Deception: Current HypothesesBody posture and gestures (Burgoon et al 94)Complete shifts in posture, touching ones face,Microexpressions (Ekman 76, Frank 03)Fleeting traces of fear, elation,Biometric factors (Horvath 73)Increased blood pressure, perspiration, respirationOdor??Changes in brain activity??Variation in what is said and how (Adams 96, Pennebaker et al 01, Streeter et al 77)

  • Spoken Cues to Deception(DePaulo et al. 03)Liars less forthcoming?-Talking time -Details +Presses lips Liars less compelling?-Plausibility -Logical Structure -Discrepant, ambivalent -Verbal, vocal involvement -Illustrators -Verbal, vocal immediacy +Verbal, vocal uncertainty +Chin raise +Word, phrase repetitions Liars less positive, pleasant?-Cooperative +Negative, complaining -Facial pleasantness Liars more tense?+Nervous, tense overall +Vocal tension +F0 +Pupil dilation+Fidgeting Fewer ordinary imperfections?-Spontaneous corrections -Admitted lack of memory +Peripheral details

  • Current Approaches to Deception DetectionTraining humansJohn Reid & AssociatesBehavioral Analysis: Interview and InterrogationLaboratory studies: Production and Perception`Automatic methodsPolygraphVoice Stress Analysis (Microtremors 8-12Hz)Nemesysco and the Love DetectorNo evidence that any of these work.but publishing this can be dangerous! (Anders Eriksson and Francisco La Cerda)

  • Little objective, experimentally verified study of spoken cues to deception which can be extracted automatically

  • OutlineResearch on DeceptionPossible cues to deceptionCurrent approachesOur corpus-based study of deceptive speechApproachCorpus collection/paradigmFeatures extractedExperiments and resultsHuman perception studiesFuture research

  • Corpus-Based Approach to Deception DetectionGoal: Identify a set of acoustic, prosodic, and lexical features that distinguish between deceptive and non-deceptive speech as well or better than human judgesMethod:Record a new corpus of deceptive/non-deceptive speech Extract acoustic, prosodic, and lexical features based on previous literature and our work in emotional speech and speaker idUse statistical Machine Learning techniques to train models to classify deceptive vs. non-deceptive speech

  • Major ObstaclesCorpus-based approaches require large amounts of training data difficult to obtain for deceptionDifferences between real world and laboratory liesMotivation and potential consequencesRecording conditionsIdentifying ground truthEthical issuesPrivacySubject rights and Institutional Review Boards

  • Columbia/SRI/Colorado Deception Corpus (CSC)Deceptive and non-deceptive speechWithin subject (32 adult native speakers)25-50m interviewsDesign:Subjects told goal was to find people similar to the 25 top entrepreneurs of AmericaGiven tests in 6 categories (e.g. knowledge of food and wine, survival skills, NYC geography, civics, music), e.g.What should you do if you are bitten by a poisonous snake out in the wilderness?Sing Casta Diva.What are the 3 branches of government?

  • Questions manipulated so scores always differed from a (fake) entrepreneur target in 4/6 categoriesSubjects then told real goal was to compare those who actually possess knowledge and ability vs. those who can talk a good gameSubjects given another chance at $100 lottery if they could convince an interviewer they matched target completelyRecorded interviewsInterviewer asks about overall performance on each test with follow-up questions (e.g. How did you do on the survival skills test?)Subjects also indicate whether each statement T or F by pressing pedals hidden from interviewer

  • The Data15.2 hrs. of interviews; 7 hrs subject speechLexically transcribed & automatically aligned Truth conditions aligned with transcripts: Global / LocalSegmentations (Local Truth/Local Lie): Words (31,200/47,188)Slash units (5709/3782)Prosodic phrases (11,612/7108)Turns (2230/1573)250+ featuresAcoustic/prosodic features extracted from ASR transcriptsLexical and subject-dependent features extracted from orthographic transcripts

  • LimitationsSamples (segments) not independent Pedal may introduce additional cognitive loadEqually for truth and lieOnly one subject reported any difficultyStakes not the highestNo fear of punishmentSelf-presentation and financial reward

  • Acoustic/Prosodic FeaturesDuration featuresPhone / Vowel / Syllable DurationsNormalized by Phone/Vowel Means, SpeakerSpeaking rate features (vowels/time)Pause features (cf Benus et al 06)Speech to pause ratio, number of long pausesMaximum pause lengthEnergy features (RMS energy)Pitch featuresPitch stylization (Sonmez et al. 98)Model of F0 to estimate speaker rangePitch ranges, slopes, locations of interestSpectral tilt features

  • Lexical FeaturesPresence and # of filled pausesIs this a question? A question following a questionPresence of pronouns (by person, case and number)A specific denial?Presence and # of cue phrasesPresence of self repairsPresence of contractionsPresence of positive/negative emotion wordsVerb tense Presence of yes, no, not, negative contractionsPresence of absolutely, reallyPresence of hedgesComplexity: syls/wordsNumber of repeated wordsPunctuation typeLength of unit (in sec and words)# words/unit length# of laughs# of audible breaths# of other speaker noise# of mispronounced words# of unintelligible words

  • Subject-Dependent Features% units with cue phrases% units with filled pauses% units with laughterLies/truths with filled pauses ratioLies/truths with cue phrases ratioLies/truths with laughter ratioGender

  • Results88 features, normalized within-speakerDiscrete: Lexical, discourse, pauseContinuous features: Acoustic, prosodic, paralinguistic, lexicalBest Performance: Best 39 features + c4.5 MLAccuracy: 70.00% LIE F-measure: 60.59 TRUTH F-measure: 75.78 Lexical, subject-dependent & speaker normalized features best predictors

  • Some ExamplesPositive emotion words deception (LIWC)Pleasantness deception (DAL)Filled pauses truthSome pitch correlations varies with subject

  • OutlineResearch on DeceptionPossible cues to deceptionCurrent approachesOur corpus-based study of deceptive speechApproachCorpus collection/paradigmFeatures extractedExperiments and resultsHuman perception studiesFuture research

  • Evaluation: Compare to Human Deception DetectionMost people are very poor at detecting deception ~50% accuracy (Ekman & OSullivan 91, Aamodt 06)People use unreliable cues, even with training

  • A Meta-Study of Human Deception Detection (Aamodt & Mitchell 2004)

    Group#Studies#SubjectsAccuracy %Criminals15265.40Secret service13464.12Psychologists450861.56Judges219459.01Cops851155.16Federal officers434154.54Students1228,87654.20Detectives534151.16Parole officers13240.42

  • Evaluating Automatic Methods by Comparing to Human PerformanceDeception detection on the CSC Corpus32 JudgesEach judge rated 2 interviewsReceived training on one subject.Pre- and post-test questionnairesPersonality Inventory

  • By Judge58.2% Acc.By Interviewee58.2% Acc.

  • What Makes Some People Better Judges?Costa & McCrae (1992) NEO-FFI Personality MeasuresExtroversion (Surgency). Includes traits such as talkative, energetic, and assertive. Agreeableness. Includes traits like sympathetic, kind, and affectionate. Conscientiousness. Tendency to be organized, thorough, and planful. Neuroticism (opp. of Emotional Stability). Characterized by traits like tense, moody, and anxious. Openness to Experience (aka Intellect or Intellect/Imagination). Includes having wide interests, and being imaginative and insightful.

  • Neuroticism, Openness & Agreeableness Correlate with Judges PerformanceOn Judging Global lies.

  • Other Useful FindingsNo effect for trainingJudges post-test confidence did not correlate with pre-test confidenceJudges who claimed experience had significantly higher pre-test confidenceBut not higher accuracyMany subjects reported using disfluencies as cues to deceptionBut in this corpus, disfluencies correlate with truth (Benus et al. 06)

  • OutlineRese