Prolonging Functional Speech in Persons with Amyotrophic Lateral Sclerosis: A Real-Time Virtual Vocal Tract

延长肌萎缩侧索硬化症患者的功能性言语：实时虚拟声带

• 批准号: 9370414
• 负责人: JORDAN R GREEN
• 金额: $ 18.98万
• 依托单位: MGH INSTITUTE OF HEALTH PROFESSIONS
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-10 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9370414
• 关键词: Address; Algorithms; Amyotrophic Lateral Sclerosis; Articulation; Articulators; Bypass; Cellular Phone; Cerebral Palsy; Characteristics; Communication; Complex; Cues; Data; Deterioration; Development; Devices; Disease; Dysarthria; Effectiveness; Electromagnetics; Ensure; Future; Generations; Goals; Impairment; Individual; Jaw; Laboratories; Learning; Lip structure; Machine Learning; Malignant neoplasm of larynx; Maps; Measures; Modeling; Modification; Motion; Motor; Movement; Multiple Sclerosis; Oral; Output; Parkinson Disease; Participant; Patients; Performance; Persons; Play; Quality of life; Questionnaires; Records; Research; Research Personnel; Running; Severities; Speech; Speech Intelligibility; Speech Sound; Speed; Stroke; Structure; Surveys; System; Tablet Computer; Tablets; Techniques; Technology; Test Result; Testing; Time; Tongue; Traumatic Brain Injury; Voice; Work; base; brain cell; clear speech; cost; efficacy testing; experience; experimental study; improved; innovation; jaw movement; laptop; motor impairment; novel; oral communication; orofacial; phrases; portability; spatiotemporal; time use; usability; virtual

People with ALS eventually and inevitably experience serious speech impairment due to progressive deterioration of brain cells that control movements of the tongue, lips and jaw. Despite the devastating consequences of this speech impairment on quality of life and survival, few options are available to assist impaired oral communication, and many existing speech-generating technologies are slow to operate and cost prohibitive. This project seeks to improve quality of life for persons with impaired speech due to ALS by testing the effectiveness of a low-cost, speech-generating device (a virtual vocal tract) that could significantly prolong the ability of these patients to communicate orally. If successful, these techniques could be extended for use by patients' with a broad range of speech motor impairments. The virtual vocal track uses machine learning algorithms to predict what a person is attempting to say, in real-time, based solely on lip movements. Users of the device are able to trigger the playback of a number of predetermined phrases by simply attempting to articulate what they want to say. Our previous work has shown the feasibility of this approach using cost-prohibitive laboratory systems such as electromagnetic articulography. Recent advances in 3D depth mapping camera technology allow these techniques to be tested for the first time using technologies, which are low-cost, portable and already being integrated into consumer devices such as laptops and cellphones. To this end, the system under development will be tested in 60 patients with ALS, representing a range of speech impairment from normal to severe speech intelligibility (15 normal, 15 mild, 15 moderate, 15 severe). During testing, participants will be cued to articulate the phrases in a random order as fast as is comfortable for them. The entire session will be recorded and the following variables will be measured offline: recognition accuracy, recognition latency, task time, % completion, and communication rate (words per minute). Users will rate the usability and acceptability of the virtual vocal tract immediately following device testing, using the System Usability Scale. Results of this testing will be used to address the following specific aims: (1) Determine the accuracy and latency of real-time phrase synthesis based on dysarthric speech using the virtual vocal tract, (2) Determine the usability and acceptability of real-time phrases produced using the virtual vocal tract, and (3) Identify the articulatory and speech factors that degrade recognition accuracy.

ALS患者最终会不可避免地经历严重的语言障碍， 控制舌头、嘴唇和下巴运动的脑细胞退化。尽管 这种语言障碍对生活质量和生存的破坏性后果，很少有选择， 可用于帮助受损的口头交流，以及许多现有的语音生成技术 操作缓慢且成本过高。该项目旨在提高残疾人的生活质量， 通过测试一种低成本的语音发生装置（a）的有效性， 虚拟声道），可以显著延长这些患者的口头交流能力。如果 如果成功的话，这些技术可以推广到具有广泛语言能力的病人身上 运动障碍 虚拟声道使用机器学习算法来预测一个人试图 比如说，实时的，仅仅基于嘴唇的运动。该设备的用户能够触发回放 通过简单地尝试清楚地表达他们想说的话来表达一些预定的短语。我们 以前的工作已经表明，使用成本高昂的实验室系统，这种方法是可行的 例如电磁关节造影术。3D深度映射相机技术的最新进展 允许这些技术首次使用低成本、便携式技术进行测试， 并且已经被集成到诸如笔记本电脑和手机之类的消费设备中。 为此，正在开发的系统将在60名ALS患者中进行测试， 语言障碍的范围从正常到严重的语言清晰度（15正常，15轻度，15 中度，15例重度）。在测试过程中，参与者将被提示以随机的方式说出这些短语。 以最快的速度为他们服务。整个会议将被记录下来， 将离线测量变量：识别准确性、识别延迟、任务时间、完成百分比， 和通信速率（每分钟的字）。用户将对 器械测试后立即使用系统可用性量表进行虚拟声道测试。结果 该测试将用于解决以下具体目标：（1）确定准确性， 使用虚拟声道的基于构音障碍语音的实时短语合成的延迟，（2） 确定使用虚拟声道产生的实时短语的可用性和可接受性， （3）识别降低识别准确率的发音和语音因素。