DESIPHER_Speech Degradation as an Indicator of Physiological Degeneration in ALS

 描述(由申请人提供)： 2008年，肌萎缩侧索硬化症(ALS)或Lou Gehrig病成为一种推定可补偿(服务相关)的疾病，因为医学研究所(IOM)委员会指出ALS的发展与服兵役之间存在联系。根据国际移民组织的报告，服兵役使肌萎缩侧索硬化症的生命风险增加1.5倍。大约有4200名退伍军人患有肌萎缩侧索硬化症，每年大约有1000例新病例。自2007年以来，在坦帕退伍军人协会，诊断和治疗肌萎缩侧索硬化症的退伍军人人数持续上升。大多数通常用于确定ALS患者功能状态的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(DESIPHER)来：识别语音病理，并使用它们来预测与ALS相关的生理性退化的其他方面(例如，呼吸困难或吞咽困难)，并最终改善语音障碍者的语音识别。我们希望这将提高我们适当识别和干预患有肌萎缩侧索硬化症的退伍军人面临呼吸衰竭和吸入等严重不良医疗问题的风险的能力。我们假设，从“正常”基线分析(受损)言语的总体分歧，将被证明比已提出的其他方法更可靠，也是更好的参与标记。这项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特定类型的语言/语音错误，以产生可供ALS患者的照顾者或医生阅读的准确文本记录？(2)ALS患者的生理功能的具体变化；强迫肺活量、舌力、语速、体重(下降)、吸入风险或心理困扰，反映在与ALS相关的不同类型的语言/语音错误中？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，有可能应用一种新的非侵入性测量来评估ALS患者的功能。此外，与语音降级相关的特征是 有可能使现有的语音识别软件随着时间的推移而适应患者的语音，从而可以通过与计算机交谈来改善患者的生活质量。呼吸衰竭是ALS患者发病和死亡的主要原因。我们预计，分析语音的方法将为呼吸功能提供一个很好的生物标志物，因为由于需要增加呼吸频率、降低响度和降低整体语音速度，语音过程中的停顿预期会增加。第二个主要死亡原因是吸入。随着关节肌肉的衰退，我们预计说话的清晰度也会降低。言语参与往往先于吞咽参与；因此，我们预计，言语分歧的增加将预示着潜在的渴望风险。

 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.