Skip to main content
SpringerLink
Log in

Contextual Perceptions of Feminine-, Masculine- and Gender-Ambiguous-Sounding Conversational Agents

  • Conference paper
  • First Online:
Information for a Better World: Shaping the Global Future (iConference 2022)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13192))

Included in the following conference series:

Abstract

The study explored whether cultural gender stereotypes are carried into the domain of conversational agents (CAs), and examined user reactions to feminine, masculine, and gender-ambiguous voices in the context of stressful and non-stressful interactions. The user’s image of an ideal CA was also investigated. A fully virtual experiment guided participants through interactions with three differently voiced Amazon Alexa test apps, collected participants’ demographics, ratings and comments about CAs performance, voice and personality manifestations on stressful, non-stressful and personality-revealing tasks. The masculine sounding agent was most frequently associated with extraverted, sensitive and open-minded personality, the gender-ambiguous agent was perceived as organized; and the feminine sounding agent as sympathetic. Most of the participants wanted their ideal CAs to have a highly warm and competent personality, and preferred this personality in both stressful and non-stressful contexts. Nearly half of the participants identified a preference for contextually dependent voice or stated a preference for an ideal agent with a gender-ambiguous voice, though this agent received the lowest scores during experimental interactions. The study contributes to the discussion of the cultural gender stereotypes in conversational technology and user preferences for agent’s voices and personality.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    To hear Matthew and Leslie’s voices visit: http://bit.ly/matthewleslievoices.

  2. 2.

    The Leslie Digital Assistant skill can be accessed through this link: https://amzn.to/2QdQEEF.

  3. 3.

    The Matthew Digital Assistant skill can be accessed through this link: https://amzn.to/3uKsbWn.

  4. 4.

    A copy of the instrument (online questionnaire) can be viewed here: http://bit.ly/lesliematthewinstrument

References

  1. Aaker, J., Vohs, K.D., Mogilner, C.: Nonprofits are seen as warm and for-profits as competent: firm stereotypes matter. J. Consum. Res. 37(2), 224–237 (2010). https://doi.org/10.1086/651566

    Article  Google Scholar 

  2. Aaker, J.L., Garbinsky, E.N., Vohs, K.D.: Cultivating admiration in brands: warmth, competence, and landing in the ‘golden quadrant.’ J. Consum. Psychol. 22(2), 191–194 (2012). https://doi.org/10.1016/j.jcps.2011.11.012

    Article  Google Scholar 

  3. “Amazon Web Services (AWS) Cloud Computing Services”, Amazon Web Services, Inc. https://aws.amazon.com/. Accessed 06 Jan 2021

  4. Anderson, R.C., Klofstad, C.A.: Preference for leaders with masculine voices holds in the case of feminine leadership roles. PLOS ONE 7(12), e51216 (2012). https://doi.org/10.1371/journal.pone.0051216

    Article  Google Scholar 

  5. Apple, W., Streeter, L.A., Krauss, R.M.: Effects of pitch and speech rate on personal attributions. J. Pers. Soc. Psychol. 37(5), 715–727 (1979)

    Article  Google Scholar 

  6. Audacity, 2020, Audacity ® (2.4.1). https://www.audacityteam.org. Accessed 01 June 2020

  7. Banai, I.P., Banai, B., Bovan, K.: Vocal characteristics of presidential candidates can predict the outcome of actual elections. Evol. Hum. Behav. 38(3), 309–314 (2017)

    Article  Google Scholar 

  8. Belin, P., Bestelmeyer, P.E.G., Latinus, M., Watson, R.: Understanding voice perception. Br. J. Psychol. 102(4), 711–725 (2011). https://doi.org/10.1111/j.2044-8295.2011.02041

    Article  Google Scholar 

  9. Bentley, F., Luvogt, C., Silverman, M., Wirasinghe, R., White, B., Lottrjdge, D.: Understanding the long-term use of smart speaker assistants. Proc. ACM Interact. Mobile Wearable Ubiquit. Technol. 2(3), 1–24 (2018). https://doi.org/10.1145/3264901

    Article  Google Scholar 

  10. Benyon, D., Mival, O.: Landscaping personification technologies: from interactions to relationships. In: Cerwinski, M., Lund, A., Tan, D., (eds.) CHI EA 08 Extended Abstracts on Human Factors in Computing Systems, pp. 3657–3662. Association for Computing Machinery, New York, NY, USA (2008). https://doi.org/10.1145/1358628.1358908

  11. Berry, D.S.: Vocal attractiveness and vocal babyishness: effects on stranger, self, and friend impressions. J. Nonverbal Behav. 14(3), 141–153 (1990). https://doi.org/10.1007/BF00996223

    Article  Google Scholar 

  12. Bishop, J., Keating, P.: Perception of pitch location within a speaker’s range: fundamental frequency, voice quality and speaker sex. J. Acoust. Soc. Am. 132(2), 1100–1112 (2012). https://doi.org/10.1121/1.4714351

    Article  Google Scholar 

  13. Briggs Myers, I.: Introduction to Type: A Guide to Understanding Your Results on the MBTI Instrument, 6th edn. CPP Inc, Mountain View, California (1998)

    Google Scholar�

  14. Budiu, R.: Mental Models for Intelligent Assistants. Nielsen Norman Group. https://www.nngroup.com/articles/mental-model-ai-assistants/. Accessed 29 Dec 2020

  15. Burgoon, J.K., Bullar, D.B., Woodall, W.G.: Nonverbal Communication: The Unspoken Dialogue. McGraw-Hill Inc., New York (1996)

    Google Scholar 

  16. Cambre, J., Kulkarni, C.: One Voice Fits All?: social implications and research challenges of designing voices for smart devices. Proc. ACM Hum. Comput. Inter. 3(CSCW), 1–19 (2019). https://doi.org/10.1145/3359325

    Article  Google Scholar 

  17. Carpenter, J.: Why project Q is more than the world’s first nonbinary voice for technology. Interactions 26(6), 56–59 (2019). https://doi.org/10.1145/3358912

    Article  Google Scholar 

  18. Cercas Curry, A., Robertson, J., Rieser, V.: Conversational Assistants and Gender Stereotypes: public perceptions and desiderata for voice personas. In: The Second Workshop on Gender Bias in Natural Language Processing COLING-GeBNLP Conference, Barcelona, Spain [online], Association for Computational Linguistics, pp. 72–78 (2020). https://www.aclweb.org/anthology/2020.gebnlp-1.7

  19. Chang, R.C.-S., Lu, H.-P., Yang, P.: Stereotypes or golden rules? Exploring likable voice traits of social robots as active aging companions for tech-savvy baby boomers in Taiwan. Comput. Hum. Behav. 84, 194–210 (2018). https://doi.org/10.1016/j.chb.2018.02.025

    Article  Google Scholar 

  20. Chaves, A.P., Gerosa, M.A.: Single or multiple conversational agents? an interactional coherence comparison. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, New York, NY, USA, pp. 1–13 (2018). https://doi.org/10.1145/3173574.3173765

  21. Cowan, B.R., et al.: “What can I help you with?”: infrequent users’ experiences of intelligent personal assistants”. In: Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services (Mobile HCI 17), ACM Press, Vienna, Austria, pp. 4–7 (2017). https://doi.org/10.1145/3098279.3098539

  22. Danielescu, A.: Eschewing gender stereotypes in voice assistants to promote inclusion. In: Proceedings of the 2nd Conference on Conversational User Interfaces, CUI 20. Association for Computing Machinery, New York, NY, USA, pp. 1–3 (2020). https://doi.org/10.1145/3405755.3406151

  23. Danielescu, A., Christian, G.: A Bot is Not a Polyglot: designing personalities for multi-lingual conversational agents. In: CHI EA 18 Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, New York, NY, USA, pp. 1–9 (2018). https://doi.org/10.1145/3170427.3174366

  24. Danielescu, A., Lovis-McMahon, D., Christian, G.: Designing for Radar Pace: a conversational system for coaching. In: ACM Computer Human Interaction (CHI 18), 22 April, Montreal QC Canada (2018). https://voiceux.wordpress.com/position-papers/

  25. Ellis, D.S.: Speech and social status in America. Soc. Forces 45(3), 431–437 (1967)

    Article  Google Scholar 

  26. Emerson, R.W.: Parametric tests, their nonparametric alternatives, and degrees of freedom. J. Vis. Impairment Blindness 110(5), 377–380 (2016). https://doi.org/10.1177/0145482X1611000511

    Article  Google Scholar 

  27. Feine, J., Gnewuch, U., Morana, S., Maedche, A.: Gender Bias in Chatbot design. In: Følstad, A., et al. (eds.) CONVERSATIONS 2019. LNCS, vol. 11970, pp. 79–93. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39540-7_6

    Chapter  Google Scholar 

  28. Fiske, S.T., Cuddy, A.J.C., Glick, P.: Universal dimensions of social cognition: warmth and competence. Trends Cogn. Sci. 11(2), 77–83 (2007). https://doi.org/10.1016/j.tics.2006.11.005

    Article� Google Scholar�

  29. Fr�hholz, S., Grandjean, D.: Processing of emotional vocalizations in bilateral inferior frontal cortex. Neurosci. Biobehav. Rev. 37(10), 2847–2855 (2013). https://doi.org/10.1016/j.neubiorev.2013.10.007

    Article  Google Scholar 

  30. Glass, R.L.: Frivolous research. Inf. Syst. Manage. 26(2), 209–210 (2009). https://doi.org/10.1080/10580530902797631

    Article  MathSciNet  Google Scholar 

  31. Honorof, D.N., Whalen, D.H.: Identification of speaker sex from one vowel across a range of fundamental frequencies. J. Acoust. Soc. Am. 128(5), 3095–3104 (2010). https://doi.org/10.1121/1.3488347

    Article  Google Scholar 

  32. Hughes, S.M., Gallup, G.G.: Why are we attracted to certain voices? Voice as an evolved medium for the transmission of psychological and biological information. In: Izdebski, K. (ed.) Emotions in the Human Voice, vol. 2, pp. 8 (2008)

    Google Scholar 

  33. Hughes, S.M., Rhodes, B.C.: Making age assessments based on voice: the impact of the reproductive viability of the speaker. J. Soc. Evol. Cult. Psychol. 4(4), 290–304 (2010). https://doi.org/10.1037/h0099282

    Article  Google Scholar 

  34. Ivy, D.K.: Gender Speak: Personal Effectiveness in Gender Communication, 5th edn. McGraw-Hill Inc, New York (2012)

    Google Scholar 

  35. Johnson, K.: Amazon says Alexa is an ESFJ (Updated). VentureBeat (2018). https://venturebeat.com/2018/10/25/amazon-says-alexa-is-an-estj/. Accessed Oct 2019

  36. Kedemey, D.: Here’s What Really Makes Microsoft’s Cortana So Amazing. Time (2015). https://time.com/3960670/windows-10-cortana/. Accessed 23 Dec 2020

  37. Kervyn, N., Fiske, S.T., Malone, C.: Brands as intentional agents framework: how perceived intentions and ability can map brand perception. J. Consum. Psychol. 22(2), 166–176 (2012). https://doi.org/10.1016/j.jcps.2011.09.006

    Article  Google Scholar 

  38. Kinsella, B., Mutchler, A.: Voice Assistant Consumer Adoption in Healthcare. Voicebot.AI (2019). https://voicebot.ai/2018/04/03/over-half-of-smartphone-owners-use-voice-assistants-siri-leads-the-pack/. Accessed 11 Mar 2020

  39. Klofstad, C.A., Anderson, R.C., Peters, S.: Sounds like a winner: voice pitch influences perception of leadership capacity in both men and women. Proc. Royal Soc. B Biol. Sci. 279(1738), 2698–2704 (2012). https://doi.org/10.1098/rspb.2012.0311

    Article  Google Scholar 

  40. Kumar, P., Jakhanwal, N., Bhowmick, A., Chandra, M.: Gender classification using pitch and formants. In: ICCCS 11 Proceedings of the 2011 International Conference on Communication, Computing & Security, 12–14 February 2011, ACM Press, Rourkela, Odisha, India, p. 319 (2011). https://doi.org/10.1145/1947940.1948007

  41. Kunst, A.: Preferences for male or female voices for digital voice assistant among residents of the United States, as of April 2017. Statista (2019). https://www.statista.com/statistics/702911/united-states-digital-voice-assistants-survey-gender-preferences/. Accessed 14 Dec 2020

  42. Lass, N.J., Hughes, K.R., Bowyer, M.D., Waters, L.T., Bourne, V.R.: Speaker sex identification from voiced, whispered, and filtered isolated vowels. J. Acoust. Soc. Am. 59(675), 675–678 (1976). https://doi.org/10.1121/1.380917

    Article  Google Scholar 

  43. Lopatovska, I.: Personality dimensions of intelligent personal assistants. In: CHIIR 20: Proceedings of the 2020 Conference on Human Information Interaction and Retrieval, 14–18 March 2020, Association for Computing Machinery, Vancouver BC Canada, pp. 333–337 (2020). https://doi.org/10.1145/3343413.3377993

  44. Lopatovska, I.: Classification of humorous interactions with intelligent personal assistants. J. Libr. Inf. Sci. 52(3), 931–942 (2020). https://doi.org/10.1177/0961000619891771

    Article  Google Scholar 

  45. Lopatovska, I., Korshakova, E., Brown, D., Li, Y., Min, J., Pasiak, A., Zheng, K.: User perceptions of an intelligent personal assistant’s personality: the role of interaction context. In: CHIIR 21: Proceedings of the 2021 Conference on Human Information Interaction and Retrieval, pp. 15–25 (2021). https://doi.org/10.1145/3406522.3446018

  46. Lopatovska, I., Oropeza, H.: User interactions with ‘Alexa’ in Public Academic Space. In: Proceedings of the Association for Information Science and Technology, 10–14 November 2018, Vancouver, Canada, pp. 309–318 (2018). https://doi.org/10.1002/pra2.2018.14505501034

  47. Lopatovska, I., Williams, H.: Personification of the Amazon Alexa: BFF or a mindless companion. In: CHIIR 18 Proceedings of the 2018 Conference on Human Information Interaction & Retrieval, 11–15 March, Association for Computing Machinery, New York, NY, USA, pp. 265–268 (2018). https://doi.org/10.1145/3176349.3176868

  48. Luria, M., Reig, S., Tan, X.Z., Steinfeld, A., Forlizzi, J., Zimmerman, J.: Re-embodiment and co-embodiment: exploration of social presence for robots and conversational agents. In: DIS 19 Proceedings of the 2019 on Designing Interactive Systems Conference, 23–28 June, Association for Computing Machinery, New York, NY, USA, pp. 633–644 (2019). https://doi.org/10.1145/3322276.3322340

  49. Manstead, A.S.R.: The psychology of social class: how socioeconomic status impacts thought, feelings, and behaviour. Br. J. Soc. Psychol. 57(2), 267–291 (2018). https://doi.org/10.1111/bjso.12251

    Article  Google Scholar 

  50. Mayew, W.J., Parsons, C.A., Venkatachalam, M.: Voice pitch and the labor market success of male chief executive officers. Evol. Hum. Behav. 34(34), 243–248 (2013). https://doi.org/10.1016/j.evolhumbehav.2013.03.001

    Article  Google Scholar 

  51. Mays, D.V.: Cross cultural social status perception in speech. Stud. Second. Lang. Acquis. 5(1), 52–64 (1982). https://doi.org/10.1017/S0272263100004599

    Article  Google Scholar 

  52. McAleer, P., Todorov, A., Belin, P.: How Do You Say ‘Hello’? Personality impressions from brief novel voices. PLoS ONE 9(3), e90779 (2014). https://doi.org/10.1371/journal.pone.0090779

    Article  Google Scholar 

  53. McCrae, R.R., Costa, P.T.: Personality in Adulthood: A Five-Factor Theory Perspective, 2nd edn. Guilford Press, New York, NY (2003)

    Book  Google Scholar 

  54. McDonnell, M., Baxter, D.: Chatbots and gender stereotyping. Interact. Comput. 31(2), 116–121 (2019). https://doi.org/10.1093/iwc/iwz007

    Article  Google Scholar 

  55. Moussawi, S., Benbunan-Fich, R.: The effect of voice and humour on users’ perceptions of personal intelligent agents. Behav. Inf. Technol. 40(3), 1–24 (2020). https://doi.org/10.1080/0144929X.2020.1772368

    Article  Google Scholar 

  56. Mulder, M.P., Nijholt, A.: Humor research: state of art. In: CTIT Technical Report Series, vol. 2, no. 02–34, Center for Telematics and Information Technology (CTIT) (2002). http://www.ub.utwente.nl/webdocs/ctit/1/0000009e.pdf

  57. Müller, S.L., Richert, A.: The big-five personality dimensions and attitudes to-wards robots: a cross sectional study. In: PETRA 18 Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference, 26–29 June, Association for Computing Machinery, New York, NY, USA, pp. 405–408 (2018). https://doi.org/10.1145/3197768.3203178

  58. Nass, C., Brave, S.: Wired for Speech: How Voice Activates and Advances the Human-Computer Relationship. The MIT Press, Cambridge (2005)

    Google Scholar 

  59. Nass, C., Moon, Y.: Machines and mindlessness: social responses to computers. J. Soc. Issues 56(1), 81–103 (2000). https://doi.org/10.1111/0022-4537.00153

    Article  Google Scholar 

  60. Nass, C., Moon, Y., Green, N.: Are machines gender neutral? Gender-stereotypic responses to computers with voices. J. Appl. Soc. Psychol. 27(10), 864–876 (1997)

    Article  Google Scholar 

  61. Nass, C., Steuer, J., Tauber, E.R.: Computers are social actors. In: CHI 94 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems Celebrating Interdependence, 24–28 April, ACM Press, Boston, Massachusetts, United States, pp. 72–78 (1994). https://doi.org/10.1145/191666.191703

  62. Norgaard, N., Equal, A.I.: Meet Q The First Genderless Voice (2019). https://www.genderlessvoice.com. Accessed 01 July 2021

  63. Norman, D.A.: How might people interact with agents. Commun. ACM 37(7), 68–71 (1994). https://doi.org/10.1145/176789.176796

    Article  Google Scholar 

  64. Obinali, C.: The perception of gender in voice assistants. In: SAIS 2019 Proceedings of the Southern Association for Information Systems Conference, 22–23 March, St. Simon’s Island, GA, USA, vol. 39, No.7, pp. 1–6 (2019). https://aisel.aisnet.org/sais2019/39/

  65. Personality, n.d.: American Psychological Association. https://www.apa.org/topics/personality. Accessed 26 Mar 2021

  66. Pittam, J.: Voice in Social Interaction: An Interdisciplinary Approach. Sage Publications, Inc., Thousand Oaks (1994)

    Book  Google Scholar 

  67. Porter, C.M., Rigby, J.R.: Relationship context and personality shape people’s preferences for network relationship partners. Pers. Relat. 26(2), 310–330 (2019). https://doi.org/10.1111/pere.12275

    Article  Google Scholar 

  68. Rammstedt, B.: The 10-item big five inventory. Eur. J. Psychol. Assess. 23(3), 193–201 (2007). https://doi.org/10.1027/1015-5759.23.3.193

    Article  Google Scholar 

  69. Rammstedt, B., John, O.P.: Measuring personality in one minute or less: a 10-item short version of the big five inventory in english and german. J. Res. Pers. 41(1), 203–212 (2007). https://doi.org/10.1016/j.jrp.2006.02.001

    Article  Google Scholar 

  70. Snyder, M., Cantor, N.: Understanding personality and social behavior: a functionalist strategy. In: Gilbert, D.T., Fiske, S.T., Lindzey, G. (eds.) The Handbook of Social Psychology, pp. 635–679. McGraw-Hill, Inc., New York (1998)

    Google Scholar 

  71. Søndergaard, M.L.J., Hansen, L.K.: Intimate futures: staying with the trouble of digital personal assistants through design fiction. In: DIS 18 Proceedings of the 2018 Designing Interactive Systems Conference, 9–13 June, Association of Computing Machinery, Hong Kong China, pp. 869–880 (2018). https://doi.org/10.1145/3196709.3196766

  72. Steele, C.: The Real Reason Voice Assistants Are Female (and Why it Matters). PCMAG (2018). https://www.pcmag.com/opinions/the-real-reason-voice-assistants-are-female-and-why-it-matters. Accessed 29 Dec 2020

  73. Stemler, S.: An overview of content analysis. Pract. Assess. Res. Eval. 7(17), 1–6 (2019). https://doi.org/10.7275/z6fm-2e34

    Article  Google Scholar�

  74. Tigue, C.C., Borak, D.J., O’Connor, J.J.M., Schandl, C., Feinberg, D.R.: Voice pitch influences voting behavior. Evol. Hum. Behav. 33(3), 210–216 (2012). https://doi.org/10.1016/j.evolhumbehav.2011.09.004

    Article  Google Scholar 

  75. UNESCO: I’d blush if I could: closing gender divides in digital skills through education. UNESCO Digital Library (2019). https://unesdoc.unesco.org/ark:/48223/pf0000367416.page=1. Accessed 23 July 2019

  76. Unkefer, H., Sophie, R.: Accenture and CereProc Introduce and Open Source the World’s First Comprehensive Non-Binary Voice Solution. Accenture Newsroom (2020). https://newsroom.accenture.com/news/accenture-and-cereproc-introduce-and-open-source-the-worlds-first-comprehensive-non-binary-voice-solution.htm. Accessed 01 July 2021

  77. Voiceflow: Design, prototype, and build voice apps. Voiceflow (0.1.0). https://www.voiceflow.com/. Accessed 06 Jan 2021

  78. Waytz, A., Heafner, J., Epley, N.: The mind in the machine: anthropomorphism increases trust in an autonomous vehicle. J. Exp. Soc. Psychol. 52, 113–117 (2014). https://doi.org/10.1016/j.jesp.2014.01.005

    Article  Google Scholar 

  79. Waytz, A., Morewedge, C.K., Epley, N., Monteleone, G., Gao, J.-H., Cacioppo, J.T.: Making sense by making sentient: effectance motivation increases anthropomorphism. J. Pers. Soc. Psychol. 99(3), 410–435 (2010). https://doi.org/10.1037/a0020240

    Article  Google Scholar 

  80. Weber, R.: Basic Content Analysis, 2nd edn. Thousand Oaks, California (2021). https://doi.org/10.4135/9781412983488

  81. Willis, J., Todorov, A.: First impressions: making up your mind after a 100-Ms exposure to a face. Psychol. Sci. 17(7), 592–598 (2006). https://doi.org/10.1111/j.1467-9280.2006.01750.x

    Article  Google Scholar 

Download references

Acknowledgment

We are grateful to our participants and the Pratt Institute for supporting the study.

Author information

Authors and Affiliations

  1. School of Information, Pratt Institute, 144 w14th street, New York, 10011, USA

    Irene Lopatovska, Diedre Brown & Elena Korshakova

Authors
  1. Irene Lopatovska
    View author publications

    You can also search for this author in PubMed�Google Scholar

  2. Diedre Brown
    View author publications

    You can also search for this author in PubMed�Google Scholar

  3. Elena Korshakova
    View author publications

    You can also search for this author in PubMed�Google Scholar

Corresponding author

Correspondence to Irene Lopatovska .

Editor information

Editors and Affiliations

  1. Humboldt-Universit�t zu Berlin, Berlin, Germany

    Malte Smits

Rights and permissions

Reprints and permissions

Copyright information

� 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lopatovska, I., Brown, D., Korshakova, E. (2022). Contextual Perceptions of Feminine-, Masculine- and Gender-Ambiguous-Sounding Conversational Agents. In: Smits, M. (eds) Information for a Better World: Shaping the Global Future. iConference 2022. Lecture Notes in Computer Science(), vol 13192. Springer, Cham. https://doi.org/10.1007/978-3-030-96957-8_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-96957-8_38

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-96956-1

  • Online ISBN: 978-3-030-96957-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation