Measuring Performance of a Predictive Keyboard Operated by Humming

  • Ondřej Poláček
  • Adam J. Sporka
  • Zdeněk Míkovec
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7383)

Abstract

A number of text entry methods use a predictive completion based on letter-level n-gram model. In this paper, we investigate on an optimal length of n-grams stored in such model for a predictive keyboard operated by humming. In order to find the length, we analyze six different corpora, from which a model is built by counting number of primitive operations needed to enter a text. Based on these operations, we provide a formula for estimation of words per minute (WPM) rate. The model and the analysis results are verified in an experiment with three experienced users of the keyboard.

Keywords

Text Entry Methods N-Gram Model Measuring Performance Non-Verbal Vocal Interaction 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Polacek, O., Mikovec, Z., Sporka, A.J., Slavik, P.: Humsher: A Predictive Keyboard Operated by Humming. In: 13th International ACM SIGACCESS Conference on Computers and Accessibility, ASSETS 2011, pp. 75–82. ACM, New York (2011)CrossRefGoogle Scholar
  2. 2.
    Igarashi, T., Hughes, J.F.: Voice as sound: using non-verbal voice input for interactive control. In: 14th Annual ACM Symposium on User Interface Software and Technology, UIST 2001, pp. 155–156. ACM, New York (2001)CrossRefGoogle Scholar
  3. 3.
    Sporka, A.J., Kurniawan, S.H., Slavík, P.: Non-speech operated emulation of keyboard. In: Clarkson, J., Langdon, P., Robinson, P. (eds.) Designing Accessible Technology, pp. 145–154. Springer, London (2006)CrossRefGoogle Scholar
  4. 4.
    Harada, S., Landay, J.A., Malkin, J., Li, X., Bilmes, J.A.: The Vocal Joystick: Evaluation of voice-based cursor control techniques. In: 8th International ACM SIGACCESS Conference on Computers and Accessibility, ASSETS 2006, pp. 197–204. ACM, New York (2006)CrossRefGoogle Scholar
  5. 5.
    Ward, D.J., Blackwell, A.F., MacKay, D.J.C.: Dasher – a data entry interface using continuous gestures and language models. In: 13th ACM Symp. on User Interface Software and Technology, UIST 2000, pp. 129–137. ACM, New York (2000)CrossRefGoogle Scholar
  6. 6.
    Kushler, C.: AAC: Using a Reduced Keyboard. Technical report (1998)Google Scholar
  7. 7.
    Felzer, T., MacKenzie, I., Beckerle, P., Rinderknecht, S.: Qanti: A Software Tool for Quick Ambiguous Non-standard Text Input. In: Miesenberger, K., Klaus, J., Zagler, W., Karshmer, A. (eds.) ICCHP 2010. LNCS, vol. 6180, pp. 128–135. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Sporka, A.J., Felzer, T., Kurniawan, S.H., Polacek, O., Haiduk, P., MacKenzie, I.S.: CHANTI: Predictive Text Entry Using Non-verbal Vocal Input. In: 2011 Annual Conference on Human Factors in Computing Systems, CHI 2011, pp. 2463–2472. ACM, New York (2011)Google Scholar
  9. 9.
    Teahan, W.: Probability estimation for PPM. In: New Zealand Research Students’ Conference (1995)Google Scholar
  10. 10.
    Vertanen, K., Kristensson, P.O.: The Imagination of Crowds: Conversational AAC Language Modeling using Crowdsourcing and Large Data Sources. In: Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, pp. 700–711. Association for Computational Linguistics (2011)Google Scholar
  11. 11.
    Vertanen, K., Kristensson, P.O.: A versatile dataset for text entry evaluations based on genuine mobile emails. In: 13th Int. Conf. on Human Computer Interaction with Mobile Devices and Services, MobileHCI 2011, pp. 198–295. ACM, New York (2011)Google Scholar
  12. 12.
    Wobbrock, J.O.: Measures of text entry performance. In: MacKenzie, I.S., Tanaka-Ishii, K. (eds.) Text Entry Systems: Mobility, Accessibility, Universality, pp. 47–74. Morgan Kaufmann (2007)Google Scholar
  13. 13.
    MacKenzie, I.S.: KSPC (Keystrokes per Character) as a Characteristic of Text Entry Techniques. In: Paternó, F. (ed.) Mobile HCI 2002. LNCS, vol. 2411, pp. 195–210. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  14. 14.
    MacKenzie, I.S.: Movement time prediction in human-computer interfaces. In: Graphics Interface 1992, Toronto, pp. 140–150. Canadian Information Processing Society (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ondřej Poláček
    • 1
  • Adam J. Sporka
    • 1
  • Zdeněk Míkovec
    • 1
  1. 1.Faculty of Electrical EngineeringCzech Technical University in PraguePrague 2Czech Republic

Personalised recommendations