Voice assistants have become an essential tool for people with various disabilities because they enable complex phone- or tablet-based interactions without the need for fine-grained motor control, such as with touchscreens. However, these systems are not tuned for the unique characteristics of individuals with speech disorders, including many of those who have a motor-speech disorder, are deaf or hard of hearing, have a severe stutter, or are minimally verbal. We introduce an alternative voice-based input system which relies on sound event detection using fifteen nonverbal mouth sounds like "pop," "click," or "eh." This system was designed to work regardless of ones' speech abilities and allows full access to existing technology. In this paper, we describe the design of a dataset, model considerations for real-world deployment, and efforts towards model personalization. Our fully-supervised model achieves segment-level precision and recall of 88.6% and 88.4% on an internal dataset of 710 adults, while achieving 0.31 false positives per hour on aggressors such as speech. Five-shot personalization enables satisfactory performance in 84.5% of cases where the generic model fails.

介绍了一种使用口腔非语言声音控制的语音输入系统，该系统适用于语音障碍者；设计了数据集，考虑了模型部署和个性化；经过测试，模型实现了较高的精度和召回率，并且在个性化的情况下工作良好。

非语言声音检测技术用于失语症言语障碍的研究