Voice in Human–Agent Interaction: A Survey
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Social robots, conversational agents, voice assistants, and other embodied AI are increasingly a feature of everyday life. What connects these various types of intelligent agents is their ability to interact with people through voice. Voice is becoming an essential modality of embodiment, communication, and interaction between computer-based agents and end-users. This survey presents a meta-synthesis on agent voice in the design and experience of agents from a human-centered perspective: voice-based human–agent interaction (vHAI). Findings emphasize the social role of voice in HAI as well as circumscribe a relationship between agent voice and body, corresponding to human models of social psychology and cognition. Additionally, changes in perceptions of and reactions to agent voice over time reveals a generational shift coinciding with the commercial proliferation of mobile voice assistants. The main contributions of this work are a vHAI classification framework for voice across various agent forms, contexts, and user groups, a critical analysis grounded in key theories, and an identification of future directions for the oncoming wave of vocal machines.
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Research on CAs is a rapidly growing field, with an increase in publications over the last few decades, especially fueled through the development of CAs such as Amazon's Alexa that build upon voice as a modality.
“Research on such agents is a rapidly growing field, with an increase in publications over the last few decades, especially fueled through the development of CAs such as Amazon's Alexa that build upon voice as a modality, so called voice bots (Schmitt et al., 2021;Seaborn et al., 2022;Kendall et al., 2020).”
From Charting the Evolution and Future of Conversational Agents: A Research Agenda Along Five Waves and New Frontiers (2024) · cites this paper as Seaborn et al., 2022
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Schöbel, S., Schmitt, A., Benner, D., Saqr, M., Janson, A., & Leimeister, J. M. (2024) · Information Systems Frontiers · doi:10.1007/s10796-023-10375-9 · Cited by 127 · see citing works
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- Authors
- Seaborn, K., Miyake, N. P., Pennefather, P., & Otake-Matsuura, M.
- Affiliations
- Tokyo Institute of Technology (JP)
- RIKEN Center for Advanced Intelligence Project (JP)
- gDial Inc., Toronto, Ontario, Canada
- RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan
- Venue
- ACM Computing Surveys
- Published
- 2021-05-03
- Volume
- 54
- Issue
- 4
- Pages
- 1-43
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W3159367349
- Cited by
- 249
- References
- 176
- Citations by year
- 2026: 59 · 2025: 69 · 2024: 50 · 2023: 37 · 2022: 31 · 2021: 3
- FWCI
- 29.64
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Computing Surveys (publishedVersion)
- Funders
- Japan Society for the Promotion of Science (20H05022,20H05574)
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Keywords
- social robots, chatbots, voice assistants, embodied AI, human-centered design, classification framework
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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