Using Speech Recognition to Enhance Communication Capabilities for Individuals with Disabilities

这项研究的目的是了解和改进个人如何利用连续单词语音识别(CSR)系统，特别强调肢体残疾人士(如脊髓损伤、严重重复应激损伤、上肢截肢和肌肉营养不良)使用此类系统输入大量自发语音。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系统的重大问题。由于识别错误的增加，或者当用户有身体残疾时，由于识别和纠正错误的难度增加，自发语音输入使困难变得更加复杂。这项研究通过对用户任务之间的关系、用户在识别和纠正错误时遇到的困难、反馈的准确性、识别错误后纠正识别错误所使用的方法以及CSR系统提供的反馈的类型和时间进行建模来解决这些问题。随后，将定义新的交互流程，以提高企业社会责任系统的有效性。PIS认为，在错误识别过程中加强用户与CSR系统之间的协作对于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，以驱动用户收到的反馈，以帮助他们完成错误识别和纠错过程。这项研究将导致模型的开发，提供对用户和CSR系统之间关系的前所未有的理解，从而为所有用户，包括身体残疾的用户，增强基于语音的人机交流。

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.