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ABSTRACT

The present study examines if and how the "gender" of computer-generated speech affects the user's perception of the computer and their conformity to the computer's recommendation. Presented with a series of social-dilemma situations, participants made a decision after listening to the computer's argument for one of the two choices in a 2 (TTS gender: male vs. female) by 2 (participant gender: male vs. female) experiment. Consistent with gender stereotypes, the male-voiced computer exerted greater influence on the user's decision than the female-voiced computer and was perceived to be more socially attractive and trustworthy. More strikingly, gendered synthesized speech triggered social identification processes, such that female subjects conformed more to the female-voiced computer, while males conformed more to the male-voiced computer (controlling for the main effect). Similar identification effects were found on social attractiveness and trustworthiness of the computer.

REFERENCES

• 1: Clifford Nass , Kwan Min Lee, Does computer-generated speech manifest personality? an experimental test of similarity-attraction, Proceedings of the SIGCHI conference on Human factors in computing systems, p.329-336, April 01-06, 2000, The Hague, The Netherlands [doi>10.1145/332040.332452]
• 2: Eagly, A. H. 1983. Gender and social influence: A social psychological analysis. American Psychologist, 38, 971-981
• 3: Nass, C.; Moon, Y.; and Green, C. 1997. Are machines gender-neutral? Gender-stereotypic responses to computers with voices. Journal of Applied Social Psychology, 27, 864-876.
• 4: Michael Macon , Andrew Cronk , Johan Wouters , Alex Kain, OGIresLPC: Diphone synthesizer using residual-excited linear prediction, Oregon Graduate Institute School of Science & Engineering, 1997
• 5: Byron Reeves , Clifford Nass, The media equation: how people treat computers, television, and new media like real people and places, Cambridge University Press, New York, NY, 1996

INDEX TERMS

Keywords:
computers are social actors, conformity, gender, social identification, text-to-speech