Finding ways to accelerate text input for individuals with profound motor impairments has been a long-standing area of research. Closing the speed gap for augmentative and alternative communication (AAC) devices such as eye-tracking keyboards is important for improving the quality of life for such individuals. Recent advances in neural networks of natural language pose new opportunities for re-thinking strategies and user interfaces for enhanced text-entry for AAC users. In this paper, we present SpeakFaster, consisting of large language models (LLMs) and a co-designed user interface for text entry in a highly-abbreviated form, allowing saving 57% more motor actions than traditional predictive keyboards in offline simulation. A pilot study with 19 non-AAC participants typing on a mobile device by hand demonstrated gains in motor savings in line with the offline simulation, while introducing relatively small effects on overall typing speed. Lab and field testing on two eye-gaze typing users with amyotrophic lateral sclerosis (ALS) demonstrated text-entry rates 29-60% faster than traditional baselines, due to significant saving of expensive keystrokes achieved through phrase and word predictions from context-aware LLMs. These findings provide a strong foundation for further exploration of substantially-accelerated text communication for motor-impaired users and demonstrate a direction for applying LLMs to text-based user interfaces.

翻译：寻找加速重度运动障碍用户文本输入的方法一直是长期研究领域。缩小眼动追踪键盘等辅助与替代通信（AAC）设备的速度差距，对改善此类用户的生活质量至关重要。自然语言神经网络领域的最新进展为重新思考AAC用户增强文本输入的策略和用户界面提供了新机遇。本文提出SpeakFaster系统，该系统由大型语言模型（LLMs）与协同设计的用户界面组成，支持高度缩写形式的文本输入。离线模拟表明，与传统预测键盘相比，该系统可节省57%以上的运动动作。一项涉及19名非AAC参与者通过手机手动打字的初步研究验证了与离线模拟一致的节省运动效果，同时对整体打字速度影响较小。针对两名肌萎缩侧索硬化症（ALS）患者的眼控打字实验及现场测试显示，由于基于上下文感知LLMs的短语和单词预测显著减少了高成本按键输入，文本输入速度较传统基线提升29-60%。这些发现为重度运动障碍用户实现大幅加速的文本通信探索提供了坚实基础，并展示了将LLMs应用于文本用户界面的发展方向。