Foundation Models in Augmentative and Alternative Communication: Opportunities and Challenges

TL;DR

This paper explores how foundation models and AI can enhance augmentative and alternative communication (AAC) by providing personalized, scalable solutions, addressing current challenges and opening new opportunities for inclusive communication.

Contribution

It introduces AMBRA, an open platform utilizing federated learning and generative AI to improve AAC materials and address customization challenges.

Findings

Proposes a novel AI-based platform for AAC customization

Highlights potential of foundation models in AAC enhancement

Outlines challenges and future directions for AI in AAC

Abstract

Augmentative and Alternative Communication (AAC) are essential techniques that help people with communication disabilities. AAC demonstrates its transformative power by replacing spoken language with symbol sequences. However, to unlock its full potential, AAC materials must adhere to specific characteristics, placing the onus on educators to create custom-tailored materials and symbols. This paper introduces AMBRA (Pervasive and Personalized Augmentative and Alternative Communication based on Federated Learning and Generative AI), an open platform that aims to leverage the capabilities of foundation models to tackle many AAC issues, opening new opportunities (but also challenges) for AI-enhanced AAC. We thus present a compelling vision--a roadmap towards a more inclusive society. By leveraging the capabilities of modern technologies, we aspire to not only transform AAC but also guide the way toward a world where communication knows no bounds.