SBIR Phase I: Activating Voice Journaling for Mental Health with Voice Biomarkers

这个小型企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是将语音语调转化为语音生物标志物，以预测疾病的存在，监测疾病或慢性病的进展或恶化，预测住院和死亡率，关注有需要的患者，从而优化护理和成本。据估计，压力、焦虑和抑郁每年使美国雇主损失500 B美元的生产力。 此外，与心理健康相关的八种风险和行为导致了15种慢性病，占全球所有慢性病总成本的80%，预计到2030年将造成47万亿美元的问题。 语音信号指示各种健康状况、情绪和疾病。 虽然可穿戴设备正在成为评估身体变量的普遍工具，但它们测量心理变量的能力仍然有限。该公司开发了一种神经网络模型，专门用于分析自然对话中的原始文本和音频，发现表明抑郁的语音模式。该公司正在推进对人类情感分类器的研究，这是该公司首次跨国际地区进行研究;该项目将联合收割机传感器输入与最先进的基于机器学习语言的模型相结合，为压力触发器设计一个超个性化的行为推荐系统，开发一种可扩展和个性化的心理健康定量测量方法，以解决简单应用程序和高级应用程序之间的市场差距。神经精神治疗这个小型企业创新研究（SBIR）第一阶段项目致力于建立一个智能语音日志平台，利用语音生物标志物来测量和预测幸福感。 该提案的主要研究目标包括：（1）通过各种智能设备（包括手机，耳机，手表，家庭和车载音频）进行直观的人机语音交互;（2）开发，训练，改进和扩展自定义神经网络;（3）创建深度强化学习模型，为用户提供相关建议和行动，以及（4）从各个日志条目建立进度的视觉表示。这项创新的预期成果是一个基于深度学习的个性化系统，可以按需扩展到智能设备，并且足够强大，可以覆盖全球不同的多元文化背景。该公司使用传感器提供客观的测量结果，使用算法支持医生和心理学家进行评估和护理，并使用非药物健康支持工具作为新兴的数字治疗类别。该奖项反映了NSF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。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to transform voice intonations into voice biomarkers to predict the existence of disease, monitor progression or deterioration of disease or chronic condition, predict hospitalization and mortality, focus on patients in need, and as a result, optimize care and cost. Stress, anxiety, and depression cost American employers an estimated $500 B annually in lost productivity. Furthermore, eight risks and behaviors associated with mental health drive 15 chronic conditions, accounting for 80% the total costs for all chronic illnesses worldwide and representing a projected $47 T problem by 2030. Voice signals indicate a variety of health conditions, emotions, and diseases. While wearables are becoming a ubiquitous tool to assess physical variables, their ability to measure psychological variables remains limited.The company has developed a neural-network model specifically to analyze raw text and audio from natural conversation, discovering speech patterns indicative of depression. The company is advancing research on human emotion classifiers, the first of its study across international geographies; this project will combine sensor inputs for state-of-the-art machine learning language-based models, design a hyper-individualized behavioral recommendation system for stress triggers, and develop a quantitative measurement on mental health that is both scalable and personalized to address the marketplace gap between simple apps and advanced neuropsychiatric treatment. This Small Business Innovation Research (SBIR) Phase I project is dedicated to building a smart voice journaling platform utilizing voice biomarkers to measure and predict well-being. The major research objectives in this proposal include (1) intuitive human-computer voice interactions through various smart devices including phones, earbuds, watches, home, and in-car audio, (2) developing, training, refining, and scaling custom neural networks, (3) creating deep reinforcement learning models to serve relevant recommendations and actions to users, and (4) building visual representations of progress from individual journal entries. The anticipated outcome of this innovation is a personalized deep learning-based system that can be scaled to smart devices on-demand and robust enough to cover diverse, multicultural backgrounds worldwide. This company is using sensors to deliver objective measurements, algorithms to support the physician and psychologist in their assessments and care delivery, and non-pharmacological health-supportive tools as an emerging category of digital therapeutics.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.