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“You made me do it”: Classification of Blame in Married Couples’ Interactions by Fusing Automatically Derived Speech and Language Information Matthew P.

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Presentation on theme: "“You made me do it”: Classification of Blame in Married Couples’ Interactions by Fusing Automatically Derived Speech and Language Information Matthew P."— Presentation transcript:

1 “You made me do it”: Classification of Blame in Married Couples’ Interactions by Fusing Automatically Derived Speech and Language Information Matthew P. Black, Panayiotis G. Georgiou, Athanasios Katsamanis, Brian R. Baucom, and Shrikanth S. Narayanan

2 Wife: You’re the one who suggested it. Wife: Oh, Bob suggested that this might help. Wife: Why? Why did you even want to get therapy? Wife: You’re the one who suggested it. Wife: I’m living at home with a roommate. Wife: You know, you’re a stranger to me now. I- I don’t- Wife: I wasn’t expecting this when we were going to get married. Husband: Are you done dumping on me yet? Husband: I- I just don’t think there is any point in talking about this. Wife: Oh, Bob suggested that this might help. Wife: You know, you’re a stranger to me now. I- I don’t- Wife: I wasn’t expecting this when we were going to get married. Examples of Blame “You made me do it”: Classification of Blame in Married Couples’ InteractionsAug. 28, / 18 Husband blaming WifeWife blaming Husband * Note that the above clips are acted. * The real couples analyzed in this study cannot be shown for privacy reasons.

3 Overview Blame conveyed through various communicative channels – Language (e.g., “you made me do it”) – Speech (e.g., prosody) – Gestures (e.g., pointing) Goal: classify extreme cases of blame behavior using audio – “Low” vs. “High” Methodology: combine 2 automatically-derived info sources – Acoustic: models how the spouses spoke – Language: models what the spouses said “You made me do it”: Classification of Blame in Married Couples’ InteractionsAug. 28, / 18

4 Why Detect Blame? Blame is oftentimes targeted in couple therapy – Can lead to escalation of negative affect and resentment [Dimidjian et al. 2008] Blame is one important behavior researched in psychology – Rely on established manual coding methods – Challenges: coders must be trained, time-consuming How can technology help? – Automatic detection of blame is scalable alternative to manual coding Behavioral Signal Processing (BSP) – Quantify and recognize abstract human behaviors in natural interaction settings relevant to psychology research 4 / 18 S. Dimidjian, C. R. Martell, and A. Christensen, Clinical Handbook of Couple Therapy, 4th ed. The Guilford Press, 2008, ch. Integrative behavioral couple therapy, pp. 73–106.

5 General Technical Challenges Blame is a complex human behavior – High-level, heterogeneous – Need to extract generalizable features Real data in real-life scenarios – Challenging for robust feature extraction and automatic speech recognition Information across multiple modalities/cues – Distributed information across various signals: acoustics, language, gestures – How to merge/combine/fuse them “You made me do it”: Classification of Blame in Married Couples’ InteractionsAug. 28, / 18

6 Previous Work [Black et al. 2010] This paper is extension of our earlier work – Classified extreme instances (low/high) for 6 behavioral codes – Only used acoustic cues “Blame” was one of the more challenging codes to predict – Ignoring important lexical cues regarding blame – Coding manual: “explicit blaming statements (e.g., ‘you made me do it’) warrant a high blame score” This paper addresses this weakness 6 / 18 M. P. Black, A. Katsamanis, C.-C. Lee, A. C. Lammert, B. R. Baucom, A. Christensen, P. G. Georgiou, and S. S. Narayanan, “Automatic classification of married couples’ behavior using audio features,” in Proc. Interspeech, 2010.

7 Corpus Real couples in 10-minute problem-solving dyadic interactions – Longitudinal study at UCLA and U. of Washington [Christensen et al. 2004] – 117 distressed couples received couples therapy for 1 year – 569 sessions (96 hours) Audio – Single channel – Far-field – Variable noise conditions Transcription (word-level) – Chronological – Speaker labels (wife/husband) – No timing information 7 / 18 Wife: then why did you ask Husband: to get us out of debt Wife: mm hmmm... Wife: then why did you ask Husband: to get us out of debt Wife: mm hmmm... A. Christensen, D.C. Atkins, S. Berns, J. Wheeler, D. H. Baucom, and L.E. Simpson. “Traditional versus integrative behavioral couple therapy for significantly and chronically distressed married couples.” J. of Consulting and Clinical Psychology, 72: , 2004.

8 Data Pre-processing Signal-to-noise ratio (SNR) estimation – Voice activity detection (VAD) [Ghosh et al. 2011] – SNR ranged from -1 dB to 26 dB Speaker diarization – Used transcriptions and SailAlign’s speech-text alignment (available on web) – Each session’s audio split into wife/husband/unknown turns Session selection – (SNR > 5 dB) && (Speaker alignment > 55%) – 372 of 569 sessions (65%) met both thresholds (62.8 hours) 8 / 18 P. K. Ghosh, A. Tsiartas, and S. S. Narayanan, “Robust voice activity detection using long-term signal variability,” IEEE Trans. Audio, Speech, and Language Processing, vol. 19, no. 3, pp 600–613, 2011.

9 Each spouse’s overall level of blame manually coded – Standardized coding manual [Heavey et al. 2002] – 9-point scale (1 = no blame, 9 = repeated blame) – Multiple trained evaluators (mean pairwise Pearson’s correlation = 0.788) Binary classification set-up (top/bottom 20%) – Low blame (70 wife + 70 husband), High blame (70 wife + 70 husband) – Leave-one-couple-out cross-validation – Gender-independent models of blame Classification Set-up 9 / 18 High Blame High Blame High Blame High Blame Low Blame Low Blame Low Blame Low Blame C. Heavey, D. Gill, and A. Christensen. Couples interaction rating system 2 (CIRS2). University of California, Los Angeles, 2002.

10 Trained 3 classifiers – Acoustic – Lexical – Fusion Methodology Overview “You made me do it”: Classification of Blame in Married Couples’ InteractionsAug. 28, / 18

11 53,000+ features extracted 1) Extracted frame-level low-level descriptors (LLDs) – Prosodic: f 0, intensity [Praat – Boersma 2001] – Spectral: 15 MFCCs, 8 MFBs [openSMILE – Eyben et al. 2010] – Voice quality: jitter, shimmer [openSMILE – Eyben et al. 2010] 2) Separate features for each spouse (wife, husband) 3) 6 temporal granularities – Global: entire session [Black et al. 2010] – Hierarchical: 0.1s, 0.5s, 1s, 5s, 10s disjoint windows [Schuller et al. 2008] 4) 14 static functionals (e.g., mean, std. dev.) Apply binary classifier – Classifier: Support Vector Machine (SVM) with linear kernel – Confidence score: class probability estimates [LIBSVM – Chang et al. 2001] Acoustic Classifier 11 / 18 B. Schuller, M. Wimmer, L. Mösenlechner, C. Kern, D. Arsic, and G. Rigoll, “Brute-forcing hierarchical functionals for paralinguistics: A waste of feature space?” in Proc. ICASSP, 2008.

12 Derived from automatic speech recognition (ASR) Lexical classifier based on “competitive” language models – Low (High) blame language models trained on text of low (high) blame spouses – Classifier: choose the blame class that is most likely – Confidence score: absolute difference in probabilities of low/high blame case Lexical Classifier (1/2) “You made me do it”: Classification of Blame in Married Couples’ InteractionsAug. 28, / 18 Low/High blame Acoustic observations of rated spouse’s speech Most likely blame class and most likely word sequence “turn of rated spouse “Blame class”-specific acoustic model “Blame class”-specific language model Generic acoustic model Unigram language model

13 Single most likely path through word lattice may not be robust – Incorporated probabilities of 100 most likely (“N-best”) paths – Assumed N-best hypotheses independent for this paper Oracle lexical classifier – Upper bound on performance of proposed ASR-derived lexical classifier – Assume perfect word recognition rate Lexical Classifier (2/2) “You made me do it”: Classification of Blame in Married Couples’ InteractionsAug. 28, / 18 n th -best path (n = 1, …, 100) Manual transcription of rated spouse

14 Complementary info from acoustic and lexical classifiers – Score-level fusion of classifiers using confidence scores Fusion classifier: another binary SVM – Inputs: “confidence score”-weighted class hypotheses Fusion Classifier “You made me do it”: Classification of Blame in Married Couples’ InteractionsAug. 28, / 18

15 Classification Results Findings – High performance of oracle lexical classifier means lexical cues are important – Lower performance of ASR lexical classifier due to ASR being challenging – Fusion classifiers able to advantageously combine partially orthogonal language and acoustic information sources “You made me do it”: Classification of Blame in Married Couples’ InteractionsAug. 28, / 18 SystemClassifierAccuracy BaselineChance140/280 = 50.0% Unimodal Acoustic223/280 = 79.6% Lexical/ASR211/280 = 75.4% Lexical/Oracle255/280 = 91.1% Fusion Acoustic + Lexical/ASR230/280 = 82.1% Acoustic + Lexical/Oracle257/280 = 91.8% WER: 40%-90% Significant differences – All classifiers significantly higher accuracy than chance (all p < 0.01) – Oracle classifiers significantly higher accuracy than non-oracle (all p < 0.01) – (Acoustic + Lexical/ASR) significantly higher than Lexical/ASR only (p < 0.05)

16 Conclusions & Future Work Modeled high-level blame behaviors from real couples by fusing automatically-derived speech and language information – Proposed acoustic classifier more robust than lexical classifier – Even with noisy ASR, lexical classifier attained 75% accuracy – Successfully separated 82% of extreme instances with fusion classifier Blaming behaviors are important cue to detect because of their significance in the context of couple therapy – Detection of blame could facilitate clinician-guided drill-down therapy Future Work – Improve individual classifiers (acoustic/lexical) and fusion classifier – Extend fusion experiments to other behavioral codes – Apply behavioral signal processing methodologies to other domains “You made me do it”: Classification of Blame in Married Couples’ InteractionsAug. 28, / 18

17 References & Software REFERENCES M. P. Black, A. Katsamanis, C.-C. Lee, A. C. Lammert, B. R. Baucom, A. Christensen, P. G. Georgiou, and S. S. Narayanan, “Automatic classification of married couples’ behavior using audio features,” in Proc. Interspeech, A. Christensen, D.C. Atkins, S. Berns, J. Wheeler, D. H. Baucom, and L.E. Simpson. “Traditional versus integrative behavioral couple therapy for significantly and chronically distressed married couples.” J. of Consulting and Clinical Psychology, vol. 72, pp , S. Dimidjian, C. R. Martell, and A. Christensen, Clinical Handbook of Couple Therapy, 4th ed. The Guilford Press, 2008, ch. Integrative behavioral couple therapy, pp. 73–106. C. Heavey, D. Gill, and A. Christensen. Couples interaction rating system 2 (CIRS2). University of California, Los Angeles, B. Schuller, M. Wimmer, L. Mösenlechner, C. Kern, D. Arsic, and G. Rigoll, “Brute-forcing hierarchical functionals for paralinguistics: A waste of feature space?” in Proc. ICASSP, SOFTWARE P. Boersma, “Praat, a system for doing phonetics by computer,” Glot International, vol. 5, no. 9/10, pp. 341–345, C. C. Chang and C. J. Lin, LIBSVM: A library for support vector machines, 2001, software available at F. Eyben, M. Wöllmer, and B. Schuller, “OpenSMILE - The Munich versatile and fast open-source audio feature extractor,” in ACM Multimedia, 2010, pp. 1459–1462. P. K. Ghosh, A. Tsiartas, and S. S. Narayanan, “Robust voice activity detection using long-term signal variability,” IEEE Trans. Audio, Speech, and Language Processing, vol. 19, no. 3, pp , A. Katsamanis, M. P. Black, P. G. Georgious, L. Goldstein, and S. S. Narayanan, “SailAlign: Robust long speech-text alignment,” in Proc. of Workshop on New Tools and Methods for Very-Large Scale Phonetics Research, “You made me do it”: Classification of Blame in Married Couples’ InteractionsAug. 28, / 18

18 Related Work at Interspeech 2011 Papers at Interspeech 2011 on same Couple Therapy corpus: Saliency detection – Mon at 10:00 – Ses1-P1 – James Gibson, Athanasios Katsamanis, Matthew Black, and Shrikanth Narayanan, Automatic identification of salient acoustic instances in couples' behavioral interactions using Diverse Density Support Vector Machines Interaction modeling – Tues at 14:30 – Ses2-S1-P – Chi-Chun Lee, Athanasios Katsamanis, Matthew Black, Brian Baucom, Panayiotis Georgiou, and Shrikanth Narayanan, An analysis of PCA-based vocal entrainment measures in married couples' affective spoken interactions “You made me do it”: Classification of Blame in Married Couples’ InteractionsAug. 28, / 18

19 Thank you! Questions?

20 Context of Work Traditional speech and language processing research – Focused on detecting more objective human processes – e.g., automatic speech recognition (ASR) Increased research on modeling more abstract human behaviors – Affect/emotions – Paralinguistic phenomena (e.g., intent) Behavioral Signal Processing (BSP) – Quantify and recognize complex human behaviors – Naturally occurring interaction settings – Psychology and health-related research “You made me do it”: Classification of Blame in Married Couples’ InteractionsAug. 28, 2011Hidden Slide

21 Recruited Couples Couples married an average of 10.0 years (SD = 7.7 years) Ages ranged from 22 to 72 years Men median age = 43 years (SD = 8.8 years) Women median age = 42 years (SD = 8.7 years) College-educated on average Men and women median level of education = 17 years (SD = 3.2 years) Ethnicity breakdown 77% Caucasian 8% African American 5% Asian or Pacific Islander 5% Latino/Latina 5% Other Hidden Slide“You made me do it”: Classification of Blame in Married Couples’ InteractionsAug. 28, 2011

22 Definition of Blame “Blame” is one of the 13 codes in the Couples Interaction Rating System [Heavey et al. 2002] Definition in coding manual: Blames, accuses, or criticizes the partner, uses critical sarcasm; makes character assassinations such as “you’re a real jackass,” “all you do is eat,” or “why are you such a jerk about it?” Explicit blaming statements (e.g., “you made me do it” or “you prevent me from doing it”), in which the spouse is the causal agent for the problem or the subject’s reactions, warrant a high score. 1 = no blame, accusations, or criticisms of partner 9 = repeated blame, accusations, or criticisms of partner “You made me do it”: Classification of Blame in Married Couples’ InteractionsAug. 28, 2011Hidden Slide

23 Normalized the pitch stream 2 ways: Mean pitch value, µ Fo, computed across session using automatic alignment Unknown regions treated as coming from one “unknown” speaker Pitch Example & Normalization “You made me do it”: Classification of Blame in Married Couples’ InteractionsAug. 28, 2011Hidden Slide

24 Separate study: trained classifier on single feature subsets Best performing feature subsets – LLDS: MFCCs and f 0 – Speaker domains: rated spouse and both spouses – Temporal granularities: all hierarchical, global Comparison of Acoustic Features Hidden Slide“You made me do it”: Classification of Blame in Married Couples’ InteractionsAug. 28, 2011

25 Low Blame Language Model vs. High Blame Language Model Most discriminative unigrams for low/high blame: High BlameLow Blame word∆ log probword∆ log prob YOU-9.61UM6.01 YOUR-4.06THAT2.67 ME-2.53I2.57 TELL-1.51WE2.36 ACCEPT-1.45THINK2.07 Hidden Slide P. G. Georgiou, M. P. Black, A. Lammert, B. Baucom and S. S. Narayanan, "That's aggravating, very aggravating": Is it possible to classify behaviors in couple interactions using automatically derived lexical features?, in Proc. of ACII, 2011.

26 Saliency Detection with Multiple Instance Learning and Diverse Density SVM J. Gibson, A. Katsamanis, M. P. Black, and S. S. Narayanan, “Automatic identification of salient acoustic instances in couples’ behavioral interactions using Diverse Density SVM,” in Proc. Interspeech (Monday at 10:00 – Session 1-P1 – Paralinguistic Information: Classification and Detection) A. Katsamanis, J. Gibson, M. P. Black, and S. S. Narayanan, “Multiple instance learning for classification of human behavior observations,” in: ACII, 2011.


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