Using Speech Recognition to Enhance Communication Capabilities for Individuals with Disabilities

本研究的目标是了解和改进个体如何利用连续词语音识别（CSR）系统，特别强调身体残疾（如C1-C8脊髓损伤，严重重复性应激损伤，上肢截肢和肌肉萎缩症）的人使用这种系统来输入大量的自发语音。虽然语音识别算法和准确率不断提高，但识别和纠正识别错误仍然是困扰CSR系统的重要问题。由于识别错误增加，或者当用户有身体残疾时，由于识别和纠正错误的困难增加，自发的语音输入使困难更加复杂。本研究通过对用户任务、用户识别和纠正错误遇到的困难、反馈准确性、识别错误后用于纠正识别错误的方法以及CSR系统提供的反馈类型和时间之间的关系进行建模来解决这些问题。随后，将定义新的交互过程，以提高企业社会责任系统的有效性。pi认为，在错误识别过程中，用户和CSR系统之间加强协作对提高这些系统的有效性至关重要：CSR系统可以提供信心注释，这些注释驱动用户收到的反馈，帮助他们完成错误识别和错误纠正过程。这项研究将导致模型的发展，为用户和CSR系统之间的关系提供前所未有的理解，从而为所有用户（包括身体残疾的用户）提供增强的基于语音的人机通信。

The goal of this research is to understand and improve how individuals utilize continuous word speech recognition (CSR) systems, with a special emphasis on the use of such systems by people with physical disabilities (such as C1-C8 spinal cord injuries, severe repetitive stress injuries, upper extremity amputations, and muscular dystrophy) to enter non-trivial quantities of spontaneous speech. While speech recognition algorithms and accuracy rates continue to improve, identifying and correcting recognition errors remain significant problems plaguing CSR systems. The difficulties are compounded with spontaneous speech input, due to increased recognition errors, or when the users have physical disabilities, due to increased difficulty in identifying and correcting errors. This research addresses these problems by modeling the relationship between user tasks, the difficulties users encounter identifying and correcting errors, feedback accuracy, methodologies used to correct recognition errors once they are identified, and the type and timing of feedback provided by the CSR system. Subsequently, new interaction processes will be defined which increase the effectiveness of CSR systems. The PIs believe increased collaboration between users and CSR systems during the error identification process is critical to increasing the effectiveness of these systems: CSR systems can provide confidence annotations which drive the feedback users receive to help them complete the error identification and error correction processes. This research will result in the development of models that provide unprecedented understanding of the relationship between users and CSR systems, thereby leading to enhanced speech based human machine communication for all users, including those with physical disabilities.