One Does Not Simply 'Mm-hmm': Exploring Backchanneling in the AAC Micro-Culture
June 22, 2025 Β· Declared Dead Β· π International ACM SIGACCESS Conference on Computers and Accessibility
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Tobias Weinberg, Claire O'Connor, Ricardo E. Gonzalez Penuela, Stephanie Valencia, Thijs Roumen
arXiv ID
2506.17890
Category
cs.HC: Human-Computer Interaction
Citations
2
Venue
International ACM SIGACCESS Conference on Computers and Accessibility
Last Checked
4 months ago
Abstract
Backchanneling (e.g., "uh-huh", "hmm", a simple nod) encompasses a big part of everyday communication; it is how we negotiate the turn to speak, it signals our engagement, and shapes the flow of our conversations. For people with speech and motor impairments, backchanneling is limited to a reduced set of modalities, and their Augmentative and Alternative Communication (AAC) technology requires visual attention, making it harder to observe non-verbal cues of conversation partners. We explore how users of AAC technology approach backchanneling and create their own unique channels and communication culture. We conducted a workshop with 4 AAC users to understand the unique characteristics of backchanneling in AAC. We explored how backchanneling changes when pairs of AAC users communicate vs when an AAC user communicates with a non-AAC user. We contextualize these findings through four in-depth interviews with speech-language pathologists (SLPs). We conclude with a discussion about backchanneling as a micro-cultural practice, rethinking embodiment and mediation in AAC technology, and providing design recommendations for timely multi-modal backchanneling while respecting different communication cultures.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted