Advertisement

Talker Quality in Design and Evaluation of Speech-Based Interactive Systems

  • Benjamin Weiss
Chapter
Part of the T-Labs Series in Telecommunication Services book series (TLABS)

Abstract

Scientific theory and practice in HCI has changed over the last decades, not only due to new insights and empirical progress, but also with the emergence of new trends affecting viewpoints and raising new research questions.

References

  1. 18.
    Bødker, S.: Third-wave HCI, 10 years later—participation and sharing. Interactions 22, 24–31 (2015)CrossRefGoogle Scholar
  2. 26.
    Baumann, T.: Incremental spoken dialogue processing: Architecture and lower-level components. Ph.D. thesis, University of Bielefeld (2013)Google Scholar
  3. 36.
    Betz, S., Carlmeyer, B., Wagner, P., Wrede, B.: Interactive hesitation synthesis: Modelling and evaluation. Multimodal Technol. Interact 2, 1–21 (2018)CrossRefGoogle Scholar
  4. 141.
    Harrison, S., Tatar, D., Sengers, P.: The three paradigms of HCI. In: Proceedings of CHI, pp. 1–18. ACM, New York (2007)Google Scholar
  5. 222.
    Levitan, R.: Acoustic-prosodic entrainment in human-human and human-computer dialogue. Ph.D. thesis, University of Columbia (2014)Google Scholar
  6. 250.
    Mc’Tear, M., Callejas, Z., Griol, D.: The Conversational Interface. Springer, Switzerland (2016)CrossRefGoogle Scholar
  7. 277.
    Pearl, C.: Designing Voice User Interfaces. O’Reilly, Beijing (2017)Google Scholar
  8. 299.
    Rieser, V., Lemon, O.: Reinforcement Learning for Adaptive Dialogue Systems. Springer, Berlin (2011)CrossRefGoogle Scholar
  9. 302.
    Rogers, I.: HCI Theory: Classical, Modern, and Contemporary. Synthesis Lectures on Human-Centered Informatics. Morgan & Claypool, Wadsworth (2012)Google Scholar
  10. 326.
    Skantze, G., Hjalmarsson, A.: Towards incremental speech generation in conversational systems. Comput. Speech Lang. 27, 243–262 (2013)CrossRefGoogle Scholar
  11. 339.
    Su, P.H., Gasic, M., Young, S.: Reward estimation for dialogue policy optimisation. Comput. Speech Lang. 51, 24–43 (2018)CrossRefGoogle Scholar
  12. 354.
    Ultes, S., Budzianowski, P., Casanueva, I., Mrkšić, N., Rojas-Barahona, L., Su, P.H., Wen, T.H., Gašić, M., Young, S.: Domain-independent user satisfaction reward estimation for dialogue policy learning. In: Proceedings of the Interspeech, pp. 1721–1725 (2017)Google Scholar
  13. 365.
    Wagner, P., Betz, S.: Speech synthesis evaluation: Realizing a social turn. In: Möbius, B., Steiner, I., Trouvain, J. (eds.) 28th Konferenz Elektronische Sprachsignalverarbeitung (ESSV), Saarbrücken, Studientexte zur Sprachkommunikation, pp. 167–173. TUD Press, Dresden (2017)Google Scholar
  14. 414.
    Wu, Z., Swietojanski, P., Veaux, C., Renals, S., King, S.: A study of speaker adaptation for DNN-based speech synthesis. In: Proceedings of the Interspeech, pp. 879–883 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Benjamin Weiss
    • 1
  1. 1.Technische Universität BerlinBerlinGermany

Personalised recommendations