DESIPHER_Speech Degradation as an Indicator of Physiological Degeneration in ALS

DESIPHER_言语退化作为 ALS 生理退化的指标

• 批准号: 9217408
• 负责人: Sam L. Phillips
• 金额: --
• 依托单位: JAMES A. HALEY VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9217408
• 关键词: Accent; Address; Affect; Amyotrophic Lateral Sclerosis; Articulation; Assessment tool; Biological Markers; Biophysics; Body Weight decreased; Breathing; Caregivers; Cause of Death; Clinical; Communication; Complex; Computer software; Computers; Controlled Environment; Data; Deglutition; Detection; Development; Diagnosis; Disease; Disease Marker; Disease Progression; Flaccid Dysarthrias; Frequencies; Future; General Population; Human; Human Resources; Impairment; Individual; Institute of Medicine (U.S.); Intervention; Language; Lead; Life; Longitudinal Studies; Loudness; Machine Learning; Measures; Medical; Methods; Military Personnel; Modeling; Monitor; Morbidity - disease rate; Movement; Muscle; Patients; Pattern; Physicians; Physiological; Population; Process; Quality of life; Readability; Reporting; Research; Research Personnel; Respiration; Respiratory Failure; Respiratory physiology; Risk; Services; Social isolation; Spastic; Speech; Speech Pathology; Speech Recognition Software; Sum; Symptoms; System; Technology; Testing; Time; Tongue; Training; Transcript; Veterans; Vital capacity; Voice; Work; functional status; improved; mortality; progression marker; psychological distress; public health relevance; respiratory; spasticity; speech recognition

 DESCRIPTION (provided by applicant): In 2008, Amyotrophic lateral sclerosis (ALS) or Lou Gehrig's Disease became a presumptively compensable (service connected) disease as the Institute of Medicine (IOM) Committee stated an association between the development of ALS and military service. According to the IOM report, military service increases life risk of ALS by 1.5 fold. There are approximately 4,200 Veterans with ALS and roughly 1,000 new cases each year. At the Tampa VA, since 2007, there has been a consistent rise in the number of Veterans diagnosed and treated with ALS. Most physiological assessments that are commonly used to determine the functional status of patients with ALS require trained clinical personnel to administer and interpret the results. We propose to use automatic speech understanding and machine learning software (DESIPHER) to: identify speech pathologies and use them to predict other aspects of physiological degeneration associated with ALS (e.g., respiratory difficulty or inability to swallow), and ultimately improve speech recognition for those with speech impairments. We expect this to improve our ability to appropriately identify and intervene when Veterans with ALS are at risk of serious adverse medical issues such as respiratory failure and aspiration. We postulate that analyzing the overall divergence of (impaired) speech, from a "normal" baseline, will prove to be more robust and a better marker for involvement than others that have been proposed. Specific research questions to be addressed by this study are: (1) Is it possible to train a speech recognition system to adapt to increasingly more frequent language/speech errors of particular types, to produce an accurate textual transcript that would be readable by an ALS patient's caregiver or physician? (2) Are specific changes in physiological functioning; Forced Vital Capacity, tongue strength, speech velocity, weight (loss), aspiration risk, or psychological distress, reflected in different types of language/speech errors associated with ALS? By understanding how speech functioning correlates with the degree to which other biophysical functioning has degraded, it is possible to apply a new, non-invasive measure for assessing the functionality of an ALS patient. In addition, the features associated with speech degradation it is possible to adapt existing speech recognition software to a patient's speech as it evolves over time, so that the quality of life for patients may be improved through conversation with a computer. Respiratory failure is the main cause of morbidity and mortality in ALS patients. We expect that the method of analyzing speech will present an excellent biomarker for respiratory function, as there is an expected increase in pauses during speech due to the necessity of increase frequency of respirations, a decrease in loudness, and decreased overall velocity of speech. A second major cause of death is aspiration. As the articular muscles decline, we expect to note a decrease in the clarity of speech. Speech involvement often precedes swallowing involvement; thus, we expect that increasing speech divergence will indicate potential aspiration risk.

 描述（由申请人提供）： 2008年，肌萎缩性侧索硬化症（ALS）或卢伽雷氏病成为一种假定可补偿（服务相关）的疾病，因为医学研究所（IOM）委员会指出ALS的发展与兵役之间的联系。根据IOM的报告，服兵役使ALS的生命风险增加了1.5倍。每年大约有4,200名退伍军人患有ALS，大约有1,000例新病例。在坦帕退伍军人管理局，自2007年以来，被诊断和治疗ALS的退伍军人人数持续上升。通常用于确定ALS患者功能状态的大多数生理评估需要训练有素的临床人员来管理和解释结果。我们建议使用自动语音理解和机器学习软件（AMPHER）来：识别语音病理，并使用它们来预测与ALS相关的生理退化的其他方面（例如，呼吸困难或无法吞咽），并最终提高那些有语言障碍的人的语言识别能力。我们希望这能提高我们的能力，适当地识别和干预时，退伍军人与ALS是在严重的不良医疗问题，如呼吸衰竭和吸入的风险。我们假设，分析的整体分歧（受损）的语音，从“正常”的基线，将被证明是更强大的，更好的标记参与比其他人已经提出。本研究要解决的具体研究问题是：（1）是否有可能训练语音识别系统，以适应日益频繁的语言/语音错误的特定类型，产生一个准确的文本转录，将可读的ALS患者的护理人员或医生？(2)生理功能的特定变化;用力肺活量，舌强度，语速，体重（损失），吸入风险或心理困扰，反映在与ALS相关的不同类型的语言/言语错误中？通过了解语言功能如何与其他生物物理功能退化的程度相关，可以应用一种新的非侵入性措施来评估ALS患者的功能。此外，与语音退化相关的特征是 可以使现有的语音识别软件适应患者的语音，因为它随着时间的推移而演变，使得患者的生活质量可以通过与计算机的对话来改善。呼吸衰竭是ALS患者发病和死亡的主要原因。我们期望分析语音的方法将呈现用于呼吸功能的极好的生物标志物，因为由于呼吸频率增加的必要性、响度降低和语音的总体速度降低，在语音期间存在预期的停顿增加。第二大死因是误吸。随着关节肌肉的衰退，我们预计会注意到言语清晰度的下降。言语受累通常先于吞咽受累;因此，我们预计言语分化的增加将表明潜在的误吸风险。