Using Speech Recognition to Enhance Communication Capabilities for Individuals with Disabilities

使用语音识别增强残疾人的沟通能力

• 批准号: 9910607
• 负责人: Andrew Sears
• 金额: $ 45.63万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-09-01 至 2003-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9910607&HistoricalAwards=false
• 关键词: Using; Speech; Recognition; Enhance; Communication

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.

本研究的目的是了解和改善个人如何利用连续单词语音识别（CSR）系统，特别强调身体残疾的人（如C1-C8脊髓损伤，严重的重复性应力损伤，上肢截肢和肌肉萎缩症）使用这种系统来输入非平凡数量的自发语音。 虽然语音识别算法和准确率不断提高，识别和纠正识别错误仍然是困扰CSR系统的重要问题。 由于识别错误的增加，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，这些困难与自发的语音输入相结合。 本研究解决了这些问题，通过建模用户任务之间的关系，用户遇到的困难，识别和纠正错误，反馈的准确性，用于纠正识别错误，一旦他们被确定的方法，以及CSR系统提供的反馈的类型和时间。 随后，将确定新的互动程序，以提高CSR系统的有效性。 PI认为，在错误识别过程中，用户和CSR系统之间的合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，推动用户收到反馈，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发展，提供前所未有的理解用户和CSR系统之间的关系，从而导致增强基于语音的人机通信的所有用户，包括那些有身体残疾的。