Automated Test of Word Recognition - Phase II

描述(申请人提供)：美国的听力专家每年要进行500多万次单词识别测试，相关费用超过1亿美元。目前，这些测试都是由训练有素的听力专家手动进行的。这项申请描述了使用临床测试录音和自动语音识别系统对受试者的反应进行评分的自动化临床语音识别测试的第二阶段开发。还将评估一种自动解释测试分数的方法。其目的是提高这些临床测试的准确性和效率，大幅降低成本，并提供一种客观的、自动的、基于证据的方法来解释结果。自动语音识别测试与自动纯音测听图(目前是STTR第二阶段项目)相结合，将对大多数听力学患者进行诊断性测试，从而解放听力专家的时间从事需要他们培训和技能的活动。培训和报销模式的当代变化对自动化临床程序产生了很高的需求。这些自动化程序是在现有的商用听力计上实施的，使用一台个人计算机来控制听力计的传送和刺激的路线。第一阶段的结果是通过对有限数量的受试者(n=9)进行训练的自动语音识别器获得的。人与机器评分的符合率估计在82-93%之间。额外的改进将把识别器的性能提高到等于或超过人与人之间的协议的水平，并为高效和准确的临床测试提供基础。在第二阶段，自动语音识别阈值测试将与常规临床实践中使用的手动方法进行比较。将开发两种不同的识别器评分策略，一种需要更多的测试时间，但不受个别说话人的差异影响，很容易适应其他语言；另一种需要较少的时间，但可能不适用于所有患者。一项试点研究将在西班牙语语音识别测试中测试该方法。

DESCRIPTION (provided by applicant): Over 5 million word recognition tests are administered annually by audiologists in the United States with an associated cost of more than $100 million. These tests are currently performed manually by highly trained audiologists. This application describes the Phase II development of automated clinical speech recognition tests using clinical test recordings and an automated speech recognition system to score the subjects' responses. A method for automatically interpreting the test scores will also be evaluated. The objectives are to increase the accuracy and efficiency of these clinical tests, substantially reduce the cost, and provide an objective, automatic, evidence-based method for interpreting the results. The automated speech recognition test in combination with the automated pure tone audiogram (currently an STTR Phase II project) will perform diagnostic testing of a majority of audiology patients, freeing the audiologists' time for activities that require their training and skill. Contemporary changes in training and reimbursement patterns create a high demand for automated clinical procedures. The automated procedures are implemented on existing commercial audiometers with a personal computer that controls the audiometer delivery and routing of stimuli. Phase I results were obtained with automatic speech recognizers that were trained on a limited number of subjects (n=9). Estimates of the agreement between human and machine scoring ranged from 82-93%. Additional refinements with benefits that are predictable from prior experience will increase recognizer performance to a level that equals or exceeds human-human agreement and provide the basis for efficient and accurate clinical tests. In Phase II, an automatic speech recognition threshold test will be compared to the manual method used in routine clinical practice. Two different recognizer scoring strategies will be developed, one that requires more test time but is independent of individual speaker differences and is easily adaptable to other languages, and one that requires less time but may not be applicable to all patients. A pilot study will test the method on a Spanish-language speech-recognition test.