Abstract
Adaptivity of intelligent environments to their surroundings provided by the ATRACO Spoken Dialogue Manager is only one means of adaptation. Recent work in Spoken Dialogue Systems focuses on the integration of user-centred adaptation means to alter the content, flow and structure of the ongoing dialogue. In this chapter, we introduce a general user-centred adaptation cycle, accompanied by two implemented adaptation approaches focusing respectively on short-term and long-term goals in human–computer interaction. After motivating the need for short-term and long-term goals to entail different adaptation mechanisms, we provide exemplary adaptation entities for each case with corresponding experiments and implementations. The short-term goal user satisfaction allows for detecting whether the user is not satisfied with the interaction and for triggering counter measures to improve the interaction. As a long-term goal, maintaining human–computer trust attempts to keep users still willing to use the system even if the interaction was confusing.
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Notes
- 1.
The Lets Go domain will be introduced in more detail in Sect. 7.4.3.2.
- 2.
The minimum number of exchanges to successfully complete the dialogue is 5.
- 3.
All results for DCR and TSR are significantly different (chi-squared test). Significant differences in ADL (unpaired t-test) and AIQ (Mann–Whitney U test) with the respective strategy to the right are on the level of α < 0. 01 for system initiative (ADL) and mixed initiative (ADL) and on the level of α < 0. 05 for adaptive (AIQ), random (ADL, AIQ), and mixed initiative (AIQ). All other comparisons between non-neighbours are significant with α < 0. 01.
References
Antoniou, G., van Harmelen, F.: Web ontology language: owl. In: Staab, S. (ed.) Handbook on Ontologies in Information Systems, pp. 76–92. Springer, Berlin (2003)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Engelbrecht, K.P., Gödde, F., Hartard, F., Ketabdar, H., Möller, S.: Modeling user satisfaction with hidden markov model. In: SIGDIAL ’09: Proceedings of the SIGDIAL 2009 Conference, pp. 170–177. Association for Computational Linguistics, Morristown, NJ, USA (2009)
Fraser, N.M.: The sundial speech understanding and dialogue project: results and implications for translation. In: Aslib Proceedings, vol. 46, pp. 141–148. MCB UP Ltd (1994)
Glass, A., McGuinness, D.L., Wolverton, M.: Toward establishing trust in adaptive agents. In: IUI ’08: Proceedings of the 13th International Conference on Intelligent User Interfaces, pp. 227–236. ACM, NY, USA (2008)
Gnjatović, M., Rösner, D.: Adaptive dialogue management in the nimitek prototype system. In: PIT ’08: Proceedings of the 4th IEEE Tutorial and Research Workshop on Perception and Interactive Technologies for Speech-Based Systems, pp. 14–25. Springer, Berlin (2008). doi:10.1007/978-3-540-69369-7_3
Hara, S., Kitaoka, N., Takeda, K.: Estimation method of user satisfaction using n-gram-based dialog history model for spoken dialog system. In: Calzolari, N., Choukri, K., Maegaard, B., Mariani, J., Odijk, J., Piperidis, S., Rosner, M., Tapias, D. (eds.) Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC’10). European Language Resources Association (ELRA), Valletta, Malta (2010)
Heinroth, T., Denich, D.: Spoken interaction within the computed world: evaluation of a multitasking adaption spoken dialogue system. In: 35th Annual IEEE Computer Software and Applications Conference (COMPSAC), Munich, Germany, pp. 134–143. IEEE (2011)
Higashinaka, R., Minami, Y., Dohsaka, K., Meguro, T.: Issues in predicting user satisfaction transitions in dialogues: Individual differences, evaluation criteria, and prediction models. In: Lee, G., Mariani, J., Minker, W., Nakamura, S. (eds.) Spoken Dialogue Systems for Ambient Environments. Lecture Notes in Computer Science, vol. 6392, pp. 48–60. Springer, Berlin (2010). doi:10.1007/978-3-642-16202-2_5
Higashinaka, R., Minami, Y., Dohsaka, K., Meguro, T.: Modeling user satisfaction transitions in dialogues from overall ratings. In: Proceedings of the SIGDIAL 2010 Conference, pp. 18–27. Association for Computational Linguistics, Tokyo, Japan (2010)
Hone, K.S., Graham, R.: Towards a tool for the subjective assessment of speech system interfaces (sassi). Nat. Lang. Eng. 6(3–4), 287–303 (2000). doi:10.1017/s1351324900002497
Kaelbling, L.P., Littman, M.L., Cassandra, A.R.: Planning and acting in partially observable stochastic domains. Artif. Intell. 101(1–2), 99–134 (1998)
Larsson, S., Traum, D.: Information state and dialogue management in the TRINDI dialogue move engine toolkit. Nat. Lang. Eng. 6, 323–340 (2000)
Lee, S., Eskenazi, M.: An unsupervised approach to user simulation: toward self-improving dialog systems. In: Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 50–59. Association for Computational Linguistics (2012)
Lee, J.D., See, K.A.: Trust in automation: designing for appropriate reliance. Hum. Factors J. Hum. Factors Ergon. Soc. 46(1), 50–80 (2004)
Lim, B.Y., Dey, A.K., Avrahami, D.: Why and why not explanations improve the intelligibility of context-aware intelligent systems. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’09, pp. 2119–2128. ACM, NY (2009)
Lindgaard, G., Dudek, C.: What is this evasive beast we call user satisfaction? Interact. Comput. 15(3), 429–452 (2003)
Litman, D., Pan, S.: Designing and evaluating an adaptive spoken dialogue system. User Model. User-Adap. Inter. 12(2–3), 111–137 (2002). doi:10.1023/a:1015036910358
Madsen, M., Gregor, S.: Measuring human-computer trust. In: Proceedings of the 11th Australasian Conference on Information Systems, pp. 6–8 (2000)
Mann, H.B., Whitney, D.R.: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18(1), 50–60 (1947)
Mayer, R.C., Davis, J.H., Schoorman, F.D.: An integrative model of organizational trust. Acad. Manag. Rev. 20(3), 709–734 (1995)
McTear, M.F.: Spoken Dialogue Technology – Toward the Conversational User Interface. Springer, London (2004)
Muir, B.M.: Trust in automation: part i. theoretical issues in the study of trust and human intervention in automated systems. In: Ergonomics, pp. 1905–1922 (1992)
Müller, F., Späth, C., Geier, T., Biundo, S.: Exploiting expert knowledge in factored POMDPs. In: Proceedings of the 20th European Conference on Artificial Intelligence (ECAI 2012), pp. 606–611 (2012)
Nothdurft, F., Honold, F., Kurzok, P.: Using explanations for runtime dialogue adaptation. In: Proceedings of the 14th ACM International Conference on Multimodal Interaction, pp. 63–64. ACM, New York (2012)
Oshry, M., Auburn, R., Baggia, P., Bodell, M., Burke, D., Burnett, D., Candell, E., Carter, J., Mcglashan, S., Lee, A., Porter, B., Rehor, K.: Voice extensible markup language (voicexml) version 2.1. Technical Report, W3C – Voice Browser Working Group (2007)
Pan, A.S., Litman, D.J., Pan, S.: Empirically evaluating an adaptable spoken dialogue system diane j. litman. In: Proceedings of the 7th International Conference on User Modeling, pp. 55–64 (1999)
Parasuraman, R., Riley, V.: Humans and automation: use, misuse, disuse, abuse. Hum. Factors J. Hum. Factors Ergon. Soc. 39(2), 230–253 (1997)
Potel, M.: MVP: model-view-presenter the taligent programming model for C++ and java. Technical Report, Taligent Inc (1996). http://www.wildcrest.com/Potel/Portfolio/mvp.pdf
Raux, A., Bohus, D., Langner, B., Black, A.W., Eskenazi, M.: Doing research on a deployed spoken dialogue system: one year of let’s go! experience. In: Proceedings of the International Conference on Speech and Language Processing (ICSLP) (2006)
Sanner, S.: Relational dynamic influence diagram language (rddl): language description (2010). Http://users.cecs.anu.edu.au/ ssanner/IPPC2011/RDDL.pdf
Schmitt, A., Schatz, B., Minker, W.: Modeling and predicting quality in spoken human-computer interaction. In: Proceedings of the SIGDIAL 2011 Conference, pp. 173–184. Association for Computational Linguistics, Portland, Oregon, USA (2011)
Schmitt, A., Schatz, B., Minker, W.: A statistical approach for estimating user satisfaction in spoken human-machine interaction. In: Proceedings of the IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), pp. 1–6. IEEE, Amman, Jordan (2011)
Schmitt, A., Ultes, S., Minker, W.: A parameterized and annotated spoken dialog corpus of the cmu let’s go bus information system. In: International Conference on Language Resources and Evaluation (LREC), pp. 3369–337 (2012)
Sidorov, M., Ultes, S., Schmitt, A.: Emotions are a personal thing: Towards speaker-adaptive emotion recognition. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 4836–4840 (2014)
Silver, D., Veness, J.: Monte-carlo planning in large POMDPs. In: NIPS, pp. 2164–2172 (2010)
Spearman, C.E.: The proof and measurement of association between two things. Am. J. Psychol. 15, 88–103 (1904)
Ultes, S., Minker, W.: Improving interaction quality recognition using error correction. In: Proceedings of the 14th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 122–126. Association for Computational Linguistics (2013). http://www.aclweb.org/anthology/W/W13/W13-4018
Ultes, S., Minker, W.: Interaction quality estimation in spoken dialogue systems using hybrid-hmms. In: Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), pp. 208–217. Association for Computational Linguistics (2014). http://www.aclweb.org/anthology/W14-4328
Ultes, S., Minker, W.: Managing adaptive spoken dialogue for intelligent environments. J. Ambient Intell. Smart Environ. 6(5), 523–539 (2014). doi:10.3233/ais-140275
Ultes, S., Heinroth, T., Schmitt, A., Minker, W.: A theoretical framework for a user-centered spoken dialog manager. In: Proceedings of the Paralinguistic Information and its Integration in Spoken Dialogue Systems Workshop, pp. 241–246. Springer, New York (2011)
Ultes, S., Schmitt, A., Minker, W.: Attention, sobriety checkpoint! can humans determine by means of voice, if someone is drunk…and can automatic classifiers compete? In: Proceedings of the 12th Annual Conference of the International Speech Communication Association (INTERSPEECH 2011), pp. 3221–3224 (2011)
Ultes, S., ElChabb, R., Minker, W.: Application and evaluation of a conditioned hidden markov model for estimating interaction quality of spoken dialogue systems. In: Mariani, J., Devillers, L., Garnier-Rizet, M., Rosset, S. (eds.) Proceedings of the 4th International Workshop on Spoken Language Dialog System (IWSDS), pp. 141–150. Springer, New York (2012)
Ultes, S., Schmitt, A., Minker, W.: Towards quality-adaptive spoken dialogue management. In: NAACL-HLT Workshop on Future directions and needs in the Spoken Dialog Community: Tools and Data (SDCTD 2012), pp. 49–52. Association for Computational Linguistics, Montréal, Canada (2012). http://www.aclweb.org/anthology/W12-1819
Ultes, S., ElChabb, R., Schmitt, A., Minker, W.: Jachmm: a java-based conditioned hidden markov model library. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013, Vancouver, BC, Canada, pp. 3213–3217. IEEE (2013)
Ultes, S., Schmitt, A., Minker, W.: On quality ratings for spoken dialogue systems – experts vs. users. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 569–578. Association for Computational Linguistics (2013)
Ultes, S., Dikme, H., Minker, W.: Dialogue management for user-centered adaptive dialogue. In: Proceedings of the 5th International Workshop On Spoken Dialogue Systems (IWSDS) (2014). http://www.uni-ulm.de/fileadmin/website_uni_ulm/allgemein/2014_iwsds/iwsds2014_lp_ultes.pdf
Ultes, S., Dikme, H., Minker, W.: First insight into quality-adaptive dialogue. In: International Conference on Language Resources and Evaluation (LREC), pp. 246–251 (2014)
Ultes, S., Kraus, M., Schmitt, A., Minker, W.: On objective performance measures of quality-adaptive spoken dialogue—and their correlation with interaction quality. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2015, submitted)
Ultes, S., Platero Sánchez, M.J., Schmitt, A., Minker, W.: Analysis of an extended interaction quality corpus. In: Proceedings of the 6th International Workshop On Spoken Dialogue Systems (IWSDS) (2015). Accepted for publication
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Williams, J.D.: At&t statistical dialog toolkit (2010). http://www2.research.att.com/sw/tools/asdt/
Williams, J.D.: Incremental partition recombination for efficient tracking of multiple dialog states. In: Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on, Dallas, Texas, pp. 5382–5385. IEEE (2010)
Williams, J., Young, S.: Partially observable markov decision processes for spoken dialog systems. Comput. Speech Lang. 21(2), 231–422 (2007)
Young, S., Schatzmann, J., Weilhammer, K., Ye, H.: The hidden information state approach to dialog management. In: IEEE International Conference on Acoustics, Speech and Signal Processing, 2007 (ICASSP 2007), vol. 4 (2007)
Young, S.J., Gačić, M., Keizer, S., Mairesse, F., Schatzmann, J., Thomson, B., Yu, K.: The hidden information state model: a practical framework for POMDP-based spoken dialogue management. Comput. Speech Lang. 24(2), 150–174 (2010)
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Nothdurft, F., Ultes, S., Minker, W. (2016). User-Centred Spoken Dialogue Management. In: Ultes, S., Nothdurft, F., Heinroth, T., Minker, W. (eds) Next Generation Intelligent Environments. Springer, Cham. https://doi.org/10.1007/978-3-319-23452-6_7
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