Evaluating Model that Predicts When People Will Speak to a Humanoid Robot and Handling Variations of Individuals and Instructions

  • Takaaki Sugiyama
  • Kazunori Komatani
  • Satoshi Sato
Part of the Signals and Communication Technology book series (SCT)


We have tackled the problem of predicting when a user is likely to begin speaking to a humanoid robot. The generality of the prediction model should be examined so that it can be applied to various users. We present two empirical evaluations demonstrating that (1) our proposed model does not depend on the specific participants whose data were used in our previous data collection, and (2) the model can handle variations of individuals and instructions. We collect a data set to which 25 human participants in general public gave labels indicating whether or not they would be likely to begin speaking to the robot. We then train a new model with the collected data and determine its performance by cross validation and open tests. We also investigate the relation of how much individual participants feel likely to speak with a model parameter and the instruction given before data collections. Results show that our model can handle the variations.



This research has been partly supported by the JST PRESTO Program.


  1. 1.
    Chao C, Thomaz A (2010) Turn-taking for human-robot interaction. In: Proceedings of the AAAI fall symposium on dialog with robots, pp 132–134Google Scholar
  2. 2.
    Duncan S (1972) Some signals and rules for taking speaking turns in conversations. J Pers Soc Psychol 23:283–292CrossRefGoogle Scholar
  3. 3.
    Kanda T, Ishiguro H, Imai M, Ono T (2004) Development and evaluation of interactive humanoid robots. Proc IEEE (Spec Iss Human Interact Rob Psychol Enrich) 92:1839–1850Google Scholar
  4. 4.
    Kendon A (1967) Some functions of gaze direction in social interaction. Acta Psychol 26:22–63CrossRefGoogle Scholar
  5. 5.
    Kim W, Ko H (2001) Noise variance estimation for Kalman filtering of noisy speech. IEICE Trans Inf Syst E84-D(1):155–160Google Scholar
  6. 6.
    Komatani K, Ueno S, Kawahara T, Okuno HG (2005) User modeling in spoken dialogue systems to generate flexible guidance. User Model User-Adap Interact 15(1):169–183CrossRefGoogle Scholar
  7. 7.
    Kruijff-Korbayov I, Cuayhuitl H, Kiefer B, Schrder M, Cosi P, Paci G, Sommavilla G, Tesser F, Sahli H, Athanasopoulos G, Wang W, Enescu V, Verhelst W (2012) Spoken language processing in a conversational system for child-robot interaction. In: Proceedings of the interspeech workshop on child-computer interaction, pp 132–134Google Scholar
  8. 8.
    Lee A, Nakamura K, Nisimura R, Saruwatari H, Shikano K (2004) Noise robust real world spoken dialogue system using GMM based rejection of unintended inputs. In: Proceedings of interspeech, pp 173–176Google Scholar
  9. 9.
    Mori M, MacDorman KF, Kageki N (2012) The uncanny valley. Rob Autom Mag 19(2):98–100CrossRefGoogle Scholar
  10. 10.
    Sacks H, Schegloff EA, Jefferson G (1974) A simplest systematics for the organization of turn-taking for conversation. Language 50(4):696–735CrossRefGoogle Scholar
  11. 11.
    Skantze G, Gustafson J (2009) Attention and interaction control in a human-human-computer dialogue setting. In: Proceedings of the SIGDIAL 2009 conference, pp 310–313Google Scholar
  12. 12.
    Sugiyama T, Komatani K, Sato S (2012) Predicting when people will speak to a humanoid robot. In: Proceedings of the international workshop on spoken dialog systemsGoogle Scholar
  13. 13.
    Vertegaal R, Slagter R, van der Veer GC, Nijholt A (2001) Eye gaze patterns in conversations: there is more to conversational agents than meets the eyes. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp 301–308Google Scholar
  14. 14.
    Yang Y (1999) An evaluation of statistical approaches to text categorization. Inf Retr 1:69–90CrossRefGoogle Scholar
  15. 15.
    Yoon S, Yoo CD (2002) Speech enhancement based on speech/noise-dominant decision. IEICE Trans Inf Syst E85-D(4):744–750.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Takaaki Sugiyama
    • 1
  • Kazunori Komatani
    • 1
  • Satoshi Sato
    • 2
  1. 1.The Institute of Scientific and Industrial ResearchOsaka UniversitySuitaJapan
  2. 2.Graduate School of EngineeringNagoya UniversityNagoyaJapan

Personalised recommendations