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.

最终患有ALS的人不可避免地会因进步而导致严重的言语障碍 控制舌头，嘴唇和下巴运动的脑细胞恶化。尽管有 这种语音障碍对生活质量和生存的毁灭性后果，几乎没有选择 可用于协助口腔交流受损，以及许多现有的语音生成技术 操作缓慢和成本率高。该项目旨在改善患者的生活质量 通过测试低成本，语音生成设备的有效性，由于ALS引起的语音受损（a 虚拟声道）可以显着延长这些患者口服交流的能力。如果 成功，这些技术可以扩展到具有广泛语音的患者使用 运动障碍。 虚拟声道使用机器学习算法来预测一个人正在尝试的 说，实时仅基于唇部运动。设备的用户能够触发播放 通过简单地试图表达他们想说的话，许多预定的短语。我们的 以前的工作表明了使用成本良好的实验室系统的这种方法的可行性 例如电磁关节摄影。 3D深度映射相机技术的最新进展 使用低成本，便携式技术，允许首次对这些技术进行测试 并且已经集成到了笔记本电脑和手机等消费设备中。 为此，正在开发的系统将在60名ALS患者中进行测试，代表A 语音障碍范围从正常到严重的语音清晰度（15正常，15轻度，15 中度，15个严重）。在测试过程中，参与者将被提出以随机阐明短语 订购尽可能快地舒适。将记录整个会话并以下内容 变量将被脱机测量：识别准确性，识别延迟，任务时间，完成％， 和沟通率（每分钟单词）。用户将评分 使用系统可用性量表在设备测试之后立即进行虚拟声道。结果 该测试将用于解决以下特定目的：（1）确定准确性和 实时短语合成的延迟基于使用虚拟声道的质心语音的延迟，（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）确定降低识别准确性的关节和语音因素。