RI: Small: Speech-Centered Robust and Generalizable Measurements of "In the Wild" Behavior for Mental Health Symptom Severity Tracking

双相情感障碍是一种常见的慢性疾病，其特征是从心境（健康）到躁狂（能量增强）和抑郁（能量降低）的病理波动。情绪转变对一个人的个人、社会、职业和财务状况都有深远的影响。目前的管理以临床为基础，依赖于提供者与患者的互动。然而，对服务需求的增加已经超过了能力，这就要求在提供保健服务方面进行根本性的变革。这个项目将创建新的算法，可以处理从智能手机使用中自然收集的语音数据，以测量行为和行为变化，并将这些测量结果与双相情感障碍症状的严重程度联系起来。这将导致产生新的早期预警信号，表明需要进行临床干预。自然行为提供了关于个人健康的大量信息。然而，在评估健康状况时，临床医生通常在基于传统医学方法（检查、实验室和调查）的护理点访问横断面医疗数据。下一代“精准健康”取决于一种包容和全面的方法，能够捕捉人们生活中健康的变化。这是高度相关的，因为1.3亿美国人患有慢性疾病，需要有效的监测策略。言语是监测情绪的一种很有前途的媒介。临床医生在评估人类疾病时主观地评估语言的形式和内容，因为语言会随着情绪和健康状态的变化而改变。然而，虽然语音很容易记录，但以语音为中心的移动监控解决方案目前还没有公开可用。这项技术既不够精确，也不够稳健。核心的挑战是信号本身：语音本质上是可变和复杂的。现有的技术不足以处理这种复杂性，限制了以语音为中心的情绪监测技术的准确性和鲁棒性。该项目将创造新颖而稳健的方法来从言语中提取情绪症状的严重程度。通过汉密尔顿抑郁评定量表（HamD）和青年躁狂症评定量表（YMRS）对情绪进行临床量化。这项技术的重点是创造一种方法，准确地提取以症状为中心的测量方法，这些方法的变化介于语言和情绪严重程度之间，并且对记录数据的环境和社会条件都很强大。这些方法将在密歇根大学现有的自然语音数据集上进行验证。这一统一将为以语音为中心的移动医疗解决方案提供关键的步骤。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Bipolar disorder is a common and chronic illness characterized by pathological swings from euthymia (healthy) to mania (heightened energy) and depression (lowered energy). Mood transitions are associated with profound consequences to one's personal, social, vocational, and financial well-being. Current management is clinic-based and dependent on provider-patient interactions. Yet, increased demand for services has surpassed capacity, calling for radical changes in the delivery of care. This project will create new algorithms that can process speech data naturally collected from smartphone use to measure behavior and changes in behaviors and to associate these measurements with the severity of the symptoms of bipolar disorder. This will lead to the creation of new early warning signs, indications that clinical intervention is needed. Natural behavior provides a wealth of information about the health an individual. However, when assessing health, clinicians typically access cross-sectional medical data at point-of-care that is based on traditional medical methods (exams, labs, and surveys). Next generation 'precision health' depends on an inclusive and holistic approach that captures changes in health as people live their lives. This is highly relevant as 130 million Americans live with chronic disease and need efficient monitoring strategies. Speech is a promising medium for monitoring mood. Clinicians subjectively assess both form and content of speech when evaluating human disease, as speech is altered by changes in mood and health states. Yet, while speech is easy to record, speech-centered mobile monitoring solutions are not currently publicly available. The technology is neither sufficiently accurate nor robust. The central challenge is the signal itself: speech is inherently variable and complex. Existing techniques are insufficient to handle this complexity, limiting the accuracy and robustness of speech-centered mood monitoring technologies. This project will create novel and robust approaches to extracting mood symptom severity measures from speech. Mood is clinically quantified via the Hamilton Depression Rating Scale (HamD) and the Young Mania Rating Scale (YMRS). The technology focuses on the creation of methods that accurately extract symptom-focused measures, whose variation lies between that of speech and mood severity, and that are robust to conditions, both environmental and social, in which the data were recorded. The methods will be validated on an existing natural speech dataset at the University of Michigan. The unification will provide critical steps towards speech-centered mHealth solutions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.