Skip to main content

Could Emotions Be Beneficial for Interaction Quality Modelling in Human-Human Conversations?

  • Conference paper
  • First Online:
Book cover Text, Speech, and Dialogue (TSD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10415))

Included in the following conference series:

Abstract

There are different metrics which are used in call centres or Spoken Dialogue Systems (SDSs) as an indicator for problem detection during the dialogue. One of such metrics is emotional state. The measurements of emotions can be a powerful indicator in different task-oriented services. Besides emotional state, there is another widely used metric: customer satisfaction (CS), which has a modification called Interaction Quality (IQ). The both models of CS and IQ may include emotional state as a feature. However, is it an actually necessary feature? Some users/customers can be very emotional, while other can be insufficiently emotional in different satisfaction categories. That is why emotional state may be not an informative feature for IQ/CS modelling. Our research is dedicated to the definition of the emotions measurements role in IQ modelling task.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://rapidminer.com/.

References

  1. Abdi, H., Williams, L.J.: Principal component analysis. WIREs Comput. Stat. 2, 433–459 (2010)

    Article  Google Scholar 

  2. Bailey, R.A.: Design of Comparative Experiments. Cambridge University Press, Cambridge (2008)

    Book  MATH  Google Scholar 

  3. le Cessie, S., Houwelingen, J.C.: Ridge estimators in logistic regression. Appl. Stat. 41(1), 191–201 (1992)

    Article  MATH  Google Scholar 

  4. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, New York (2000)

    Book  MATH  Google Scholar 

  5. Eyben, F., Weninger, F., Gross, F., Schuller, B.: Recent developments in opensmile, the Munich open-source multimedia feature extractor. In: Proceedings of ACM Multimedia (MM), pp. 835–838 (2013)

    Google Scholar 

  6. Gholap, J.: Performance tuning of J48 algorithm for prediction of soil fertility. Asian J. Comput. Sci. Inf. Technol. 2(8), 251–252 (2012)

    Google Scholar 

  7. Goutte, C., Gaussier, E.: A probabilistic interpretation of precision, recall and F-Score, with implication for evaluation. In: Losada, D.E., Fernández-Luna, J.M. (eds.) ECIR 2005. LNCS, vol. 3408, pp. 345–359. Springer, Heidelberg (2005). doi:10.1007/978-3-540-31865-1_25

    Chapter  Google Scholar 

  8. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutmann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)

    Article  Google Scholar 

  9. John, G.H., Langley, P.: Estimating continuous distribution in Bayesian classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338–345 (1995)

    Google Scholar 

  10. Kennedy, J.J., Bush, A.J.: An Introduction to the Design and Analysis of Experiments in Behavioural Research. University Press of America, Lanham (1985)

    Google Scholar 

  11. Maar, B., Neely, A.: Managing and Measuring for Value: the Case of Call Centre Performance. Cranfield School of Management, cranfield (2004)

    Google Scholar 

  12. Park, Y., Gates, S.C.: Towards real-time measurement of customer satisfaction using automatically generated call transcripts. In: Proceedings of the 18th ACM conference on Information and knowledge management, pp. 1387–1396 (2009)

    Google Scholar 

  13. Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Schoelkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods: Support Vector Learning. MIT Press, Cambridge (1999)

    Google Scholar 

  14. Quinkan, J.R.: C4.5: Programs for Machime Learning. Morgan Kaufmann Publishers, San Francisco (1993)

    Google Scholar 

  15. Rosenberg, A.: Classifying skewed data: importance to optimize average recall. In: Proceedings of INTERSPEECH 2012, pp. 2242–2245 (2012)

    Google Scholar 

  16. Rosenblatt, F.: Principles of Neurodynamics Perceptrons and the Theory of Brain Mechanisms. Spartan Books, Washingtion DC (1961)

    MATH  Google Scholar 

  17. 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 (2011)

    Google Scholar 

  18. Schmitt, A., Ultes, S.: Interaction quality: assessing the quality of ongoing spoken dialog interaction by experts - and how it relates to user satisfaction. Speech Commun. 74, 12–36 (2015)

    Article  Google Scholar 

  19. Schmitt, A., Ultes, S., Minker, W.: A parameterized and annotated corpus of the CMU let’s go bus information system. In: International Conference on Language Resources and Evaluation (LREC), pp. 3369–3373 (2012)

    Google Scholar 

  20. Schuller, B., Steidl, S., Batliner, A.: The interspeech 2009 emotion challenge. In: Proceedings of INTERSPEECH 2009, pp. 312–315 (2009)

    Google Scholar 

  21. Sidorov, M., Brester, C., Schmitt, A.: Contemporary stochastic feature selection algorithms for speech-based emotion recognition. In: Proceedings of INTERSPEECH 2015, pp. 2699–2703 (2015)

    Google Scholar 

  22. Spirina, A., Sidorov, M., Sergienko, R., Schmitt, A.: First experiments on interaction quality modelling for human-human conversation. In: Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics (ICINCO), vol. 2, pp. 374–380 (2016)

    Google Scholar 

  23. Spirina, A., Vaskovskaia, O., Sidorov, M., Schmitt, A.: Interaction quality as a human-human task-oriented conversation performance. In: Ronzhin, A., Potapova, R., Németh, G. (eds.) SPECOM 2016. LNCS (LNAI), vol. 9811, pp. 403–410. Springer, Cham (2016). doi:10.1007/978-3-319-43958-7_48

    Chapter  Google Scholar 

  24. Spirina, A.V., Sidorov, M.Y., Sergienko, R.B., Semenkin, E.S., Minker, W.: Human-human task-oriented conversations corpus for interaction quality modelling. Vestnik SibSAU 17(1), 84–90 (2016)

    Google Scholar 

  25. Ultes, S., Platero Sánchez, M.J., Schmitt, A., Minker, W.: Analysis of an extended interaction quality corpus. In: Lee, G.G., Kim, H.K., Jeong, M., Kim, J.-H. (eds.) Natural Language Dialog Systems and Intelligent Assistants, pp. 41–52. Springer, Cham (2015)

    Chapter  Google Scholar 

  26. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    Book  MATH  Google Scholar 

  27. Wang, J.: From customer satisfaction to emotions: alternative framework to understand customer’s post-consumption behaviour. In: Proceedings of the 2012 International Joint Conference on Service Sciences, pp. 120–124 (2012)

    Google Scholar 

  28. Wilcoxon, F.: Individual comparisons by ranking methods. Biom. Bull. 1, 80–83 (1945)

    Article  Google Scholar 

  29. Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2011)

    Google Scholar 

Download references

Acknowledgments

The work presented in this paper was partially supported by the DAAD (German Academic Exchange Service), the Ministry of Education and Science of Russian Federation within project 28.697.2016/2.2, and the Transregional Collaborative Research Centre SFB/TRR 62 “Companion-Technology for Cognitive Technical Systems” which is funded by the German Research Foundation (DFG).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anastasiia Spirina .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Spirina, A., Minker, W., Sidorov, M. (2017). Could Emotions Be Beneficial for Interaction Quality Modelling in Human-Human Conversations?. In: Ekštein, K., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2017. Lecture Notes in Computer Science(), vol 10415. Springer, Cham. https://doi.org/10.1007/978-3-319-64206-2_50

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-64206-2_50

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64205-5

  • Online ISBN: 978-3-319-64206-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics