FAME: Exploring Expressive Facial Avatars for Lyrical and Non-Lyrical Music Visualization for d/Deaf Individuals

要旨

d/Deaf and Hard of Hearing (DHH) individuals often engage with music through a multimodal approach, where visual modalities are also used rather than relying on sound alone. While tools like captions and visualizers offer partial support, they often fail to capture the emotional depth and structural nuances of music. To explore new possibilities, we adopted an iterative, probe-based approach. Through a formative study with 9 DHH participants, we identified key design requirements for visualizing rhythm, emotion, and lyrics. We developed FAME (Facial Avatar for Musical Expression), a design probe that conveys music through expressive facial animation, instrument highlights, and synchronized captions, lip-syncing to lyrics or scat-singing to melodies. Through a two-phase exploratory study with 12 DHH users, we examined FAME’s efficacy, applicability, and requirements for representing musical elements. Our findings refine design requirements for avatar-based systems and highlight the potential of avatars as expressive and socially meaningful tools for music accessibility.

著者
Suhyeon Yoo
University of Toronto, Toronto, Ontario, Canada
Yifang Pan
University of Toronto, Toronto, Ontario, Canada
Ashish Ajin Thomas
University of Toronto, Toronto, Ontario, Canada
Karan Singh
University of Toronto, Toronto, Ontario, Canada
Khai N.. Truong
University of Toronto, Toronto, Ontario, Canada

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: Sound, Music, and Dance Accessibility

P1 - Room 120
7 件の発表
2026-04-15 20:15:00
2026-04-15 21:45:00