Social Media through Voice: Synthesized Voice Qualities and Self-presentation

要旨

With advances in expressive speech synthesis and conversational understanding, an ever-increasing amount of digital content---including social and personal content---can be consumed through voice. Voice has long been known to convey personal characteristics and emotional states, both of which are prominent aspects of social media. Yet, no study has investigated voice design requirements for social media platforms. We interviewed 15 active social media users about their preferences on using synthesized voices to represent their profiles. Our findings show that participants want to have control over how a voice delivers their content, such as the personality and emotion with which the voice speaks, because these prosodic variations can impact users' online persona and interfere with impression management. We report motivations behind customizing or not customizing voice characteristics in different scenarios, and uncover key challenges around usability and the potential for stereotyping. We argue that synthesized speech for social media should be evaluated not only on listening experience and voice quality but also on its expressivity, degree of customizability, and ability to adapt to contexts (e.g., social media platforms, groups, individual posts). We discuss how our contribution confirms and extends knowledge of voice technology design and online self-presentation, and offer design considerations for voice personalization related to social interactions.

著者
Lotus Zhang
University of Washington, Seattle, Washington, United States
Lucy Jiang
University of Washington, Seattle, Washington, United States
Nicole Washington
Human Centered Design and Engineering, University of Washington, Seattle, Washington, United States
Augustina Ao. Liu
University of Washington, Seattle, Washington, United States
Jingyao Shao
Human Centered Design and Engineering, University of Washington, Seattle, Washington, United States
Adam Fourney
Microsoft Research, Redmond, Washington, United States
Meredith Ringel. Morris
Microsoft Research, Redmond, Washington, United States
Leah Findlater
論文URL

https://doi.org/10.1145/3449235

動画

会議: CSCW2021

The 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing

セッション: Voice and Speech

Papers Room B
8 件の発表
2021-10-26 20:30:00
2021-10-26 22:00:00