AUTOMATIC ANALYSIS AND DIAGNOSIS OF DYSARTHRIA

构音障碍的自动分析与诊断

• 批准号: 6016961
• 负责人: KAREN V CHENAUSKY
• 金额: $ 10.13万
• 依托单位: SPEECH TECHNOLOGY/APPLIED RESEARCH CORP.
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-09-30 至 2000-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6016961
• 关键词: biomedical automation; clinical research; computer assisted diagnosis; computer program /software; computer system design /evaluation; diagnosis design /evaluation; dysarthria; human subject; human therapy evaluation; phonology; psychoacoustics; speech disorder diagnosis; speech recognition

Differential diagnosis of subtypes of dysarthria is important because it enables clinicians to determine the location of the lesion responsible for the disorder and allows clinician and patient to set appropriate treatment goals. Even more important is a reliable, objective method of documenting treatment efficacy. Perceptual methods of evaluating dysarthria are most common. Yet differences between experts are larger than the differences needed for diagnosis or monitoring and are hard to quantify. We propose an automatic acoustic-feature recognition system to aid clinicians in evaluating dysarthria. The system and two phoneticians will be trained to judge acoustic features from sustained vowels and diadochokinetic syllables spoke by subjects with a variety of dysarthrias. The system will make its judgement from acoustic features like periodicity of glottal pulses or rate and regularity of diadochokinetic syllables, and others. The phoneticians will make their judgements based on perceptual features like breathiness, diplophonia, harshness, harshness, and others. The goal of Phase I is to show that the proposed system can identify the acoustic characteristics of different subtypes of dysarthria at 90 percent accuracy with respect to human judgements of the associated perceptual features. PROPOSED COMMERCIAL APPLICATION There is a clear need for an efficient, reliable, and objective tool to evaluate the speech of dysarthric individuals. The software could be packaged with existing acoustic analysis systems which are currently purchased by hospitals, clinics, and universities for non-automatic analysis of dysarthric speech.

构音障碍亚型的鉴别诊断很重要，因为它使临床医生能够确定导致该疾病的病变的位置，并允许临床医生和患者设定适当的治疗目标。更重要的是记录治疗效果的可靠、客观的方法。评估构音障碍的知觉方法是最常见的。然而，专家之间的差异大于诊断或监测所需的差异，而且很难量化。我们提出了一个自动的声学特征识别系统来帮助临床医生评估构音障碍。该系统和两名语音学家将接受培训，从患有各种构音障碍的受试者所说的持续元音和动态音节中判断声学特征。该系统将根据声学特征，如声门脉冲的周期性或转音运动音节的频率和规律性等进行判断。语音学家将根据呼吸、重音、刺耳、刺耳等知觉特征进行判断。第一阶段的目标是表明，相对于人类对相关知觉特征的判断，所提出的系统可以以90%的准确率识别不同亚型构音障碍的声学特征。拟议的商业应用显然需要一种有效、可靠和客观的工具来评估构音障碍患者的言语。该软件可以与现有的声学分析系统打包在一起，这些系统目前由医院、诊所和大学购买，用于非自动分析构音障碍。