Abstract
People living with Motor Neuron Disease (plwMND) frequently encounter speech and motor impairments that necessitate a reliance on augmentative and alternative communication (AAC) systems. This paper tackles the main challenge that traditional symbol-based AAC systems offer a limited vocabulary, while text entry solutions tend to exhibit low communication rates. To help plwMND articulate their needs about the system efficiently and effectively, we iteratively design and develop a novel multimodal text generation system called ImageTalk through a tailored proxy-user-based and an end-user-based design phase. The system demonstrates pronounced keystroke savings of 95.6%, coupled with consistent performance and high user satisfaction. We distill three design guidelines for AI-assisted text generation systems design and outline four user requirement levels tailored for AAC purposes, guiding future research in this field.
Abstract (translated)
患有运动神经元疾病（plwMND）的人常常面临言语和运动障碍，这需要依赖辅助性和替代性沟通（AAC）系统。本文探讨了传统基于符号的AAC系统词汇量有限的问题，而文本输入解决方案往往表现出较低的通信效率。为了帮助plwMND用户有效地表达他们对系统的反馈，我们通过定制的代理用户基础设计阶段和最终用户基础设计阶段迭代地设计并开发了一种新型多模态文本生成系统——ImageTalk。该系统展示了显著的按键节省率（95.6%），同时保持了稳定的性能和高用户满意度。我们提炼出三项针对AI辅助文本生成系统的界面设计理念，并概述了四个针对AAC目的定制的用户需求层次，为这一领域的未来研究提供了指导方针。
URL
https://arxiv.org/abs/2512.09610
