Neuroadaptive interfaces tailor speech support to personal needs
Toward Neuroadaptive Communication Interfaces: A Framework for Personalized Speech and Language Support
Human-Computer InteractionComputation and Language
Summary
Communication tools often use the same kind of help for everyone, but that can sometimes confuse or slow people down. The authors suggest a new system that changes how language is shown based on how each person interacts and what they need at the moment. They use behavior signals mainly and brain signals (EEG) as extra clues to better support the user. The study will compare regular, behavior-based, and brain-aware systems to see if adding brain signals really helps people understand and communicate better.
What this means in practice
- •For assistive technology developers: Create communication aids that dynamically adjust language presentation based on user behavior and brain signals for better individual support.$Commercial implications: Enables personalized speech and language devices for people with communication difficulties, providing more effective and adaptable assistance than fixed aids.
- •For human-computer interaction designers: Design interfaces that use behavioral and neural feedback to adapt content delivery to users’ changing comprehension needs in real time.
Authors
Carlos Bruno, Luis Ricardo Santos
Abstract
Speech and language technologies have expanded access to communication, but the support they provide is often fixed. The same linguistic or visual aid may help one user and hinder another, or become less useful as task demands change. This project proposes a framework for adapting language presentation to individual abilities and ongoing interaction. It separates access to a message from the estimation of user needs and from the selection of support, including sentence segmentation, pictograms, and optional spoken output. Behavioral feedback provides the primary evidence for adaptation; electroencephalography is considered an additional, uncertainty-aware source of information rather than a direct measure of comprehension. We specify a staged evaluation comparing fixed, behavior-adaptive, and neuroadaptive interfaces. The central question is whether neural information improves comprehension and communication efficiency beyond a strong behavioral baseline.