CAREER: Automatic Speech-Based Longitudinal Emotion and Mood Recognition for Mental Health Monitoring and Treatment

对精神健康障碍患者进行有效治疗和监测是一项持久的社会挑战。定期监测可增加获得预防性治疗的机会，但由于对卫生保健提供者的高要求，往往成本过高或不可行。然而，它对双相情感障碍（BPD）患者至关重要，BPD是一种慢性精神疾病，以健康状态和病理状态之间的情绪转变为特征。向病理状态的转变与个人、社会、职业功能和情绪调节的深刻破坏有关。这个教师早期职业发展计划（Career）项目通过利用语言、情感和情绪之间的联系，研究基于语言的情绪监测的新方法。该方法包括处理短期变化（言语）的数据，估计中期变化（情绪），然后利用情绪模式识别长期变化（情绪）。教育推广活动包括一个设计挑战，由非营利科学教育机构彩虹（Iridescent）合作，在非正式的学习环境中向缺乏教育的儿童及其父母教授情感识别。本研究探讨了自然的、纵向的语音数据建模方法，并将情感模式与情绪联系起来，解决了语音情感识别和辅助技术当前面临的挑战，包括：可泛化性、鲁棒性和性能。这些方法可以推广到症状包括非典型情绪的情况，如创伤后应激障碍、焦虑、抑郁和压力。研究提出情绪是简化言语与情绪映射的中间步骤；情绪失调是常见的BPD症状。随着时间的推移，情绪在效价和激活方面被量化，以提高归纳性。干扰调制被控制以提高鲁棒性。总之，它们形成了一系列低维度的次要特征，这些特征的变化是由情绪引起的。这些次要特征被分割，以创建更粗略的情感时间描述。这提供了一种在语音（快速变化的信号）和用户状态（缓慢变化的信号）之间进行映射的方法，从而推进了最先进的技术。研究结果为情绪变化与用户状态变化之间的关系提供了定量的洞察，为情绪识别和辅助技术领域之间的联系提供了新的方向和联系。使用时间序列技术对情感数据建模的关注导致了情感识别和辅助技术算法设计的突破。

Effective treatment and monitoring for individuals with mental health disorders is an enduring societal challenge. Regular monitoring increases access to preventative treatment, but is often cost prohibitive or infeasible given high demands placed on health care providers. Yet, it is critical for individuals with Bipolar Disorder (BPD), a chronic psychiatric illness characterized by mood transitions between healthy and pathological states. Transitions into pathological states are associated with profound disruptions in personal, social, vocational functioning, and emotion regulation. This Faculty Early Career Development Program (CAREER) project investigates new approaches in speech-based mood monitoring by taking advantage of the link between speech, emotion, and mood. The approach includes processing data with short-term variation (speech), estimating mid-term variation (emotion), and then using patterns in emotion to recognize long-term variation (mood). The educational outreach includes a design challenge, created with Iridescent, a science education nonprofit, that teaches emotion recognition to underserved children and their parents in informal learning settings. The research investigates methods to model naturalistic, longitudinal speech data and associate emotion patterns with mood, addressing current challenges in speech emotion recognition and assistive technology that include: generalizability, robustness, and performance. The approaches generalize to conditions whose symptoms include atypical emotion, such as post-traumatic stress disorder, anxiety, depression, and stress. The research forwards emotion as an intermediate step to simplify the mapping between speech and mood; emotion dysregulation is a common BPD symptom. Emotion is quantified over time in terms of valence and activation to improve generalizability. Nuisance modulations are controlled to improve robustness. Together, they result in a set of low-dimensional secondary features whose variations are due to emotion. These secondary features are segmented to create a coarser temporal description of emotion. This provides a means to map between speech (a quickly varying signal) and user state (a slowly varying signal), advancing the state-of-the-art. The results provide quantitative insight into the relationship between emotion variation and user state variation, providing new directions and links between the fields of emotion recognition and assistive technology. The focus on modeling emotional data using time series techniques results in breakthroughs in the design of emotion recognition and assistive technology algorithms.