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Let’s talk! speaking virtual counselor offers you a brief intervention

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Abstract

We developed a virtual counseling system which can deliver brief alcohol health interventions via a 3D anthropomorphic speech-enabled interface—a new field for spoken dialog interactions with intelligent virtual agents in the health domain. We present our spoken dialog system design and its evaluation. We developed our dialog system based on Markov decision processes framework and optimized it by using reinforcement learning algorithms with data we collected from real user interactions. The system begins to learn optimal dialog strategies for initiative selection and for the type of confirmations that it uses during the interaction. We compared the unoptimized system with the optimized system in terms of objective measures (e.g. task completion) and subjective measures (e.g. ease of use, future intention to use the system) and obtained positive results.

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Notes

  1. http://www.w3.org/TR/speech-grammar/.

  2. http://www.w3.org/TR/semantic-interpretation/.

  3. Microsoft Speech Recognizer.

  4. http://www.nuecho.com/en/.

  5. https://www.mturk.com.

  6. From the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSMIV), American Psychiatric Association.

  7. Conventionally, a p value less than 0.05 is considered statistically significant, a p value less than 0.10 is considered indication of a statistical trend.

References

  1. Babor TF, Grant M (1992) Programme on substance abuse: project on identification and management of alcohol-related problems. Report on phase II, an randomized clinical trial of brief interventions in primary health care

  2. Bickmore T, Gruber A (2010) Relational agents in clinical psychiatry. Harv Rev Psychiatry 18(2):119–130

    Article  Google Scholar 

  3. Bickmore TW, Puskar K, Pfeifer LM, Sereika SM (2010) Maintaining reality: relational agents for antipsychotic medication adherence. Interact Comput 22(4):276–288

    Article  Google Scholar 

  4. Bickmore TW, Schulman D, Sidner CL (2011) A reusable framework for health counseling dialogue systems based on a behavioral medicine ontology. J Biomed Inf 44(2):183–197

    Article  Google Scholar 

  5. Bickmore TW (2005) Establishing and maintaining long-term human-computer relationships. ACM Trans Comput Hum 12(2):293–327

    Article  Google Scholar 

  6. Bohus D, Rudnicky AI (2009) The ravenclaw dialog management framework: architecture and systems. Comput Speech Lang 23(3):332–361

    Article  Google Scholar 

  7. Bouchery EE, Harwood HJ, Sacks JJ, Simon CJ, Brewer RD (2011) Economic costs of excessive alcohol consumption in the US, 2006. Am J Prev Med 41(5):516–524

    Article  Google Scholar 

  8. Chandramohan S, Geist M, Pietquin O (2010) Sparse approximate dynamic programming for dialog management. In: Proceedings of the 11th annual meeting of the special interest group on discourse and dialogue, SIGDIAL’10. Association for Computational Linguistics, Stroudsburg, pp 107–115

  9. Chi M, Van Lehn K, Litman D (2010) Do micro-level tutorial decisions matter: applying reinforcement learning to induce pedagogical tutorial tactics. In: Intelligent tutoring systems. Springer, Berlin, pp 224–234

  10. de Rosis F, Novielli N, Carofiglio V, Cavalluzzi A, De Carolis B (2006) User modeling and adaptation in health promotion dialogs with an animated character

  11. Ferguson G, Allen JF et al (1998) Trips: an integrated intelligent problem-solving assistant. In: Proceedings of the national conference on artificial intelligence. Wiley, New York pp 567–573

  12. Ferguson G, Quinn J, Horwitz C, Swift M, Allen J, Galescu L (2010) Towards a personal health management assistant. J Biomed Inf 43(5 Suppl):S13–S16

    Article  Google Scholar 

  13. Frampton M, Lemon O (2009) Recent research advances in reinforcement learning in spoken dialogue systems. Knowl Eng Rev 24(4):375–408

    Article  Google Scholar 

  14. Georgila K, Henderson J, Lemon O (2006) User simulation for spoken dialogue systems, learning and evaluation. In: In Interspeech/ICSLP

  15. Georgila K, Wolters MK, Moore JD (2010) Learning dialogue strategies from older and younger simulated users. In: Proceedings of the 11th annual meeting of the special interest group on discourse and dialogue, SIGDIAL’10. Association for Computational Linguistics, Stroudsburg, pp 103–106

  16. Hone KS, Graham R (2000) Towards a tool for the subjective assessment of speech system interfaces (SASSI). Nat Lang Eng 6(3,4):287–303

    Article  Google Scholar 

  17. Henderson J, Lemon O, Georgila K (2008) Hybrid reinforcement/supervised learning of dialogue policies from fixed data sets. Comput Linguist 34(4):487–511

    Article  Google Scholar 

  18. Hester RK, Squires DD, Delaney HD (2005) The drinker’s check-up: 12-month outcomes of a controlled clinical trial of a stand-alone software program for problem drinkers. J Subst Abuse Treat 28(2):159–169

    Article  Google Scholar 

  19. Humeniuk R, Henry-Edwards S, Ali R, Poznyak V, Monteiro MG, World Health Organization et al (2010) The assist-linked brief intervention for hazardous and harmful substance use: a manual for use in primary care/prepared by R. Humeniuk et al. 2010

  20. Jurčíček F, Thomson B, Young S (2012) Reinforcement learning for parameter estimation in statistical spoken dialogue systems. Comput Speech Lang 26(3):168–192

    Article  Google Scholar 

  21. Kennedy CM, Powell J, Payne TH, Ainsworth J, Boyd A, Buchan I (2012) Active assistance technology for health-related behavior change: an interdisciplinary review. J Med Internet Res 14(3)

  22. Lemon O, Pietquin O et al (2007) Machine learning for spoken dialogue systems. In: Proceedings of the European conference on speech communication and technologies (Interspeech’07), pp 2685–2688

  23. Levin E, Levin A (2006) Evaluation of spoken dialogue technology for real-time health data collection. J Med Internet Res 8(4):e30

    Article  Google Scholar 

  24. Levin E, Pieraccini R, Eckert W (1998) Using markov decision process for learning dialogue strategies. In: Proceedings of the 1998 IEEE international conference on acoustics, speech and signal processing, vol 1. IEEE Press, New York, pp 201–204

  25. Li W, Glass J, Roy N, Teller S (2013) Probabilistic dialogue modeling for speech-enabled assistive technology. In: Proceedings of the fourth workshop on speech and language processing for assistive technologies. Association for Computational Linguistics, Grenoble, pp 67–72

  26. Lisetti C, Amini R, Yasavur U, Rishe N (2013) I can help you change! an empathic virtual agent delivers behavior change health interventions. ACM TMIS 4(4):19

    Google Scholar 

  27. Miller WR, Wilbourne PL (2002) Mesa grande: a methodological analysis of clinical trials of treatments for alcohol use disorders. Addiction 97(3):265–277

    Article  Google Scholar 

  28. Misu T, Georgila K, Leuski A, Traum D (2012) Reinforcement learning of question-answering dialogue policies for virtual museum guides. In: Proceedings of the 13th annual meeting of the special interest group on discourse and dialogue. Association for Computational Linguistics, pp 84–93

  29. Mokdad AH, Marks JS, Stroup DF, Gerberding JL (2004) Actual causes of death in the united states, 2000. JAMA 291(10):1238–1245

    Article  Google Scholar 

  30. Morbini F, DeVault D, Sagae K, Gerten J, Nazarian A, Traum D (2012) FLoReS: a forward looking, reward seeking, dialogue manager. In: 4th international workshop on spoken dialog systems, Paris

  31. Morbini F, Forbell E, DeVault D, Sagae K, Traum DR, Rizzo AA (2012) A mixed-initiative conversational dialogue system for healthcare. In: Proceedings of the 13th annual meeting of the special interest group on discourse and dialogue, SIGDIAL’12. Association for Computational Linguistics, Stroudsburg, pp 137–139

  32. Moyer A, Finney JW, Swearingen CE, Vergun P (2002) Brief interventions for alcohol problems: a meta-analytic review of controlled investigations in treatment-seeking and non-treatment-seeking populations. Addiction 97(3):279–292

    Article  Google Scholar 

  33. NIAAA (2006) Niaaa alcohol alert no. 66: Brief interventions

  34. NIAAA (2007) Helping patients who drink too much: a clinician’s guide

  35. NIAAA (2007) What colleges need to know now: an update on college drinking research

  36. Paek T, Pieraccini R (2008) Automating spoken dialogue management design using machine learning: an industry perspective. Speech Commun 50(8):716–729

    Article  Google Scholar 

  37. Papangelis A, Kouroupas N, Karkaletsis V, Makedon F (2012) An adaptive dialogue system with online dialogue policy learning. In: Proceedings of the 7th hellenic conference on artificial Intelligence: theories and applications, SETN’12. Springer, Berlin, pp 323–330

  38. Portnoy DB, Scott-Sheldon LJ, Johnson BT, Carey MP (2008) Computer-delivered interventions for health promotion and behavioral risk reduction: a meta-analysis of 75 randomized controlled trials, 1988–2007. Prev Med 47(1):3–16

    Article  Google Scholar 

  39. Riper H, Spek V, Boon B, Conijn B, Kramer J, Martin-Abello K, Smit F (2011) Effectiveness of e-self-help interventions for curbing adult problem drinking: a meta-analysis. J Med Internet Res 13(2):e42

    Article  Google Scholar 

  40. Saitz R, Palfai TP, Freedner N, Winter MR, Macdonald A, Lu J, Ozonoff AL, Rosenbloom DL, Dejong W (2007) Screening and brief intervention online for college students: the ihealth study. Alcohol Alcohol 42(1):28–36

    Article  Google Scholar 

  41. Schatzmann J, Georgila K, Young S (2005) Quantitative evaluation of user simulation techniques for spoken dialogue systems. In: 6th SIGdial workshop on DISCOURSE and DIALOGUE

  42. Schulman D, Bickmore TW, Sidner CL (2011) An intelligent conversational agent for promoting long-term health behavior change using motivational interviewing. In: Association for the advancement of artificial intelligence (AAAI) spring symposium series. Association for the advancement of artificial intelligence, pp 61–64. http://www.aaai.org

  43. Singh S, Kearns M, Litman DJ, Walker MA et al (2000) Empirical evaluation of a reinforcement learning spoken dialogue system. In: AAAI/IAAI, pp 645–651

  44. Singh S, Litman D, Kearns M, Walker M (2002) Optimizing dialogue management with reinforcement learning: experiments with the NJFun system. J Artif Intell Res 16:105–133

    Google Scholar 

  45. Steinberg KL, Roffman RA, Carroll KM, McRee B, Babor TF, Miller M, Kadden R, Duresky D, Stephens R (2005) Brief counseling for marijuana dependence: a manual for treating adults. Center for substance abuse treatment, substance abuse and mental health services administration, Rockville

  46. Sutton RS, Barto AG (1998) Reinforcement learning: an introduction, vol 1. Cambridge University Press, Cambridge

  47. Sutton S, Cole R, De Villiers J, Schalkwyk J, Vermeulen P, Macon M, Yan Y, Kaiser E, Rundle B, Shobaki K et al (1998) Universal speech tools: the CSLU toolkit. In: Proceedings of the international conference on spoken language processing (ICSLP). Sydney, pp 3221–3224

  48. Traum D, Larsson S (2003) The Information state approach to dialogue management. Kluwer Academic, New York, pp 325–353

  49. Turunen M, Hakulinen J, Ståhl O, Gambäck B, Hansen P, de la Cámara R, Smith C, Charlton D, Cavazza M (2011) Multimodal and mobile conversational health and fitness companions. Comput Speech Lang 25(2):192–209

    Article  Google Scholar 

  50. Walters ST, Hester RK, Chiauzzi E, Miller E (2005) Demon rum: high-tech solutions to an age-old problem. Alcohol Clin Exp Res 29(2):270–277

    Article  Google Scholar 

  51. Walters ST, Neighbors C (2005) Feedback interventions for college alcohol misuse: what, why and for whom? Addict Behav 30(6):1168–1182

    Article  Google Scholar 

  52. Ward W (1991) Understanding spontaneous speech: the phoenix system. In: Proceedings of the acoustics, speech, and signal processing. ICASSP-91, 1991 international conference, ICASSP’91. IEEE Computer Society, Washington, DC, pp 365–367

  53. White A, Kavanagh D, Stallman H, Klein B, Kay-Lambkin F, Proudfoot J, Drennan J, Connor J, Baker A, Hines E, Young R (2010) Online alcohol interventions: a systematic review. J Med Internet Res 12(5):e62

    Article  Google Scholar 

  54. Williams JD (2008) The best of both worlds: unifying conventional dialog systems and pomdps. In: INTERSPEECH, pp 1173–1176

  55. Williams JD, Young S (2007) Partially observable markov decision processes for spoken dialog systems. Comput Speech Lang 21(2):393–422

    Article  Google Scholar 

  56. Young S, Gasic M, Thomson B, Williams JD (2013) Pomdp-based statistical spoken dialog systems: a review. Proc. EEE 101(5):1160–9219, doi:10.1109/JPROC.2012.2225812

  57. Young S, Gašić M, Keizer S, Mairesse F, Schatzmann J, Thomson B, Yu K (2010) The hidden information state model: a practical framework for pomdp-based spoken dialogue management. Comput Speech Lang 24(2):150–174

    Article  Google Scholar 

  58. Young S, Gašić M, Keizer S, Mairesse F, Schatzmann J, Thomson B, Yu K (2010) The hidden information state model: a practical framework for pomdp-based spoken dialogue management. Comput Speech Lang 24(2):150–174

    Article  Google Scholar 

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Acknowledgments

Part of this research was funded by grants from the National Science Foundation HRD-0833093, IIP-1338922, IIP- 1237818.

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Authors and Affiliations

  1. School of Computing and Information Sciences, Florida International University, Miami, FL, 33199, USA

    Ugan Yasavur, Christine Lisetti & Naphtali Rishe

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  1. Ugan Yasavur
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  2. Christine Lisetti
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Correspondence to Ugan Yasavur.

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Yasavur, U., Lisetti, C. & Rishe, N. Let’s talk! speaking virtual counselor offers you a brief intervention. J Multimodal User Interfaces 8, 381–398 (2014). https://doi.org/10.1007/s12193-014-0169-9

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  • DOI: https://doi.org/10.1007/s12193-014-0169-9

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