It’s not What It Speaks, but It’s How It Speaks: A Study into Smartphone Voice-User Interfaces (VUI)

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9170)

Abstract

Since voice-user interfaces (VUI) are becoming an attractive tool for more intuitive user interactions, this study proposes a between-subject experiment in which variations in voice characteristics (i.e., voice gender and manner) of VUI are examined as key determinants of user perceptions. This study predicts that the voice gender (male vs. female) and manner (calm vs. exuberant) are likely to have significant effects on psychological and behavior outcomes, including credibility and trustworthiness of information delivered via VUI.

Keywords

Voice user interface Voice gender Voice manner Smart device Credibility Trust 

Notes

Acknowledgment

This research was supported by the Ministry of Education, South Korea, under the Brain Korea 21 Plus Project (Grant No. 10Z20130000013).

References

  1. 1.
    Addington, D.W.: The effect of vocal variations on ratings of source credibility. Speech Monogr. 38, 242–247 (1971)CrossRefGoogle Scholar
  2. 2.
    Sundar, S.S., Nass, C.: Source orientation in human-computer interaction programmer, networker, or independent social actor. Commun. Res. 27(6), 683–703 (2000)CrossRefGoogle Scholar
  3. 3.
    Morishima, Y., Nass, C., Bennett, C., Lee, K.M.: Effects of ‘gender’ of computer-generated speech on credibility. Technical report of IEICE TL2001-16, 31(8), pp. 557-562 (2001)Google Scholar
  4. 4.
    Reeves, B., Nass, C.: How People Treat Computers, Television, and New Media Like Real People and Places. CSLI Publications and Cambridge University Press, New York (1996)Google Scholar
  5. 5.
    Ko, S.J., Judd, C.M., Blair, I.V.: What the voice reveals: within-and between-category stereotyping on the basis of voice. Pers. Soc. Psychol. Bull. 32(6), 806–819 (2006)CrossRefGoogle Scholar
  6. 6.
    Leigh, T.W., Summers, J.O.: An initial evaluation of industrial buyers’ impressions of salespersons’ nonverbal cues. J. Pers. Selling Sales Manage. 22(1), 41–53 (2002)Google Scholar
  7. 7.
    Vacher, M., Fleury, A., Portet, F., Serignat, J.F., Noury, N.: Complete sound and speech recognition system for health smart homes: application to the recognition of activities of daily living. In: New Developments in Biomedical Engineering, pp. 645–673 (2010)Google Scholar
  8. 8.
    Rougui, J.E., Istrate, D., Souidene, W.: Audio sound event identification for distress situations and context awareness. In: Annual of the IEEE International Conference on Engineering in Medicine and Biology Society, EMBC 2009, pp. 3501–3504. IEEE (2009)Google Scholar
  9. 9.
    Lines, L., Hone, K.S.: Multiple voices, multiple choices: older adults’ evaluation of speech output to support independent living. Gerontechnology 5(2), 78–91 (2006)CrossRefGoogle Scholar
  10. 10.
    Gödde, F., Möller, S., Engelbrecht, K.P., Kühnel, C., Schleicher, R., Naumann, A., Wolters, M.: Study of a speech-based smart home system with older users. In: International Workshop on Intelligent User Interfaces for Ambient Assisted Living, pp. 17–22 (2008)Google Scholar
  11. 11.
    Hamill, M., Young, V., Boger, J., Mihailidis, A.: Development of an automated speech recognition interface for personal emergency response systems. J. NeuroEngineering Rehabil. 6(1), 26 (2009)CrossRefGoogle Scholar
  12. 12.
    López-Cózar, R., Callejas, Z.: Multimodal dialogue for ambient intelligence and smart environments. In: Nakashima, H., Aghajan, H., Augusto, J.C. (eds.) Handbook of ambient intelligence and smart environments, pp. 559–579. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Stevens, C., Lees, N., Vonwiller, J., Burnham, D.: On-line experimental methods to evaluate text-to-speech (TTS) synthesis: effects of voice gender and signal quality on intelligibility, naturalness and preference. Comput. Speech Lang. 19(2), 129–146 (2005)CrossRefGoogle Scholar
  14. 14.
    Lines, L., Hone, K.S.: Multiple voices, multiple choices: older adults’ evaluation of speech output to support independent living. Gerontechnology 5(2), 78–91 (2006)CrossRefGoogle Scholar
  15. 15.
    Mullennix, J.W., Stern, S.E., Wilson, S.J., Dyson, C.L.: Social perception of male and female computer synthesized speech. Comput. Hum. Behav. 19(4), 407–424 (2003)CrossRefGoogle Scholar
  16. 16.
    Mairesse, F., Walker, M.A., Mehl, M.R., Moore, R.K.: Using linguistic cues for the automatic recognition of personality in conversation and text. J. Artif. Intell. Res. 30, 457–500 (2007)Google Scholar
  17. 17.
    Roe, D.B., Wilpon, J.G. (eds.): Voice Communication Between Humans and Machines. National Academies Press, Washington, DC (1994)Google Scholar
  18. 18.
    Brann, M., Himes, K.L.: Perceived credibility of male versus female television newscasters. Commun. Res. Rep. 27(3), 243–252 (2010)CrossRefGoogle Scholar
  19. 19.
    Niculescu, A., Van Dijk, B., Nijholt, A., See, S.L.: The influence of voice pitch on the evaluation of a social robot receptionist. In: 2011 International Conference on User Science and Engineering (i-USEr), pp. 18–23. IEEE (2011)Google Scholar
  20. 20.
    Nass, C., Lee, K.M.: Does computer-synthesized speech manifest personality? Experimental tests of recognition, similarity-attraction, and consistency-attraction. J. Exp. Psychol. Appl. 7(3), 171 (2001)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Interaction ScienceSungkyunkwan UniversitySeoulKorea

Personalised recommendations