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

职业：基于语音的自动纵向情感和情绪识别，用于心理健康监测和治疗

• 批准号: 1651740
• 负责人: Emily Provost
• 金额: $ 54.88万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1651740&HistoricalAwards=false
• 关键词: CAREER; Automatic; Speech; Based; Longitudinal

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.

对精神健康障碍患者的有效治疗和监测是一项持久的社会挑战。定期监测增加了获得预防性治疗的机会，但由于对医疗保健提供者提出了很高的要求，因此成本往往过高或不可行。然而，这对于双相情感障碍 (BPD) 患者至关重要，双相情感障碍是一种慢性精神疾病，其特点是情绪在健康状态和病理状态之间转换。向病态状态的转变与个人、社会、职业功能和情绪调节的深刻破坏有关。该教师早期职业发展计划 (CAREER) 项目利用言语、情绪和情绪之间的联系，研究基于言语的情绪监测的新方法。该方法包括处理具有短期变化（语音）的数据，估计中期变化（情绪），然后使用情绪模式来识别长期变化（情绪）。教育推广活动包括与科学教育非营利组织 Iridescent 共同发起的设计挑战，该挑战在非正式学习环境中向服务不足的儿童及其父母教授情感识别。该研究研究了对自然纵向语音数据进行建模并将情感模式与情绪关联起来的方法，解决了当前语音情感识别和辅助技术面临的挑战，包括：通用性、鲁棒性和性能。 这些方法适用于症状包括非典型情绪的病症，例如创伤后应激障碍、焦虑、抑郁和压力。该研究将情感作为简化言语和情绪之间映射的中间步骤；情绪失调是 BPD 的常见症状。随着时间的推移，情绪会根据效价和激活进行量化，以提高普遍性。 控制干扰调制以提高鲁棒性。 它们共同产生了一组低维次要特征，其变化是由于情感造成的。 这些次要特征被分段以创建情感的更粗略的时间描述。 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。 研究结果提供了对情绪变化与用户状态变化之间关系的定量洞察，为情绪识别和辅助技术领域之间提供了新的方向和联系。 对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。

项目成果

Read speech voice quality and disfluency in individuals with recent suicidal ideation or suicide attempt

阅读最近有自杀意念或自杀企图的个人的语音质量和不流畅性

• DOI: 10.1016/j.specom.2021.05.004
• 发表时间: 2021
• 期刊: Speech Communication
• 影响因子: 3.2
• 作者: Stasak, Brian;Epps, Julien;Schatten, Heather T.;Miller, Ivan W.;Provost, Emily Mower;Armey, Michael F.
• 通讯作者: Armey, Michael F.

Self-Ensembling Attention Networks: Addressing Domain Shift for Semantic Segmentation

• DOI: 10.1609/aaai.v33i01.33015581
• 发表时间: 2019-07
• 期刊: ArXiv
• 作者: Yonghao Xu;Bo Du;Lefei Zhang;Qian Zhang;Guoli Wang;Liangpei Zhang

Into the Wild: Transitioning from Recognizing Mood in Clinical Interactions to Personal Conversations for Individuals with Bipolar Disorder

• DOI: 10.21437/interspeech.2019-2698
• 发表时间: 2019-01-01
• 期刊: INTERSPEECH 2019
• 作者: Matton, Katie;McInnis, Melvin G.;Provost, Emily Mower
• 通讯作者: Provost, Emily Mower

Exploiting Acoustic and Lexical Properties of Phonemes to Recognize Valence from Speech

• DOI: 10.1109/icassp.2019.8683190
• 发表时间: 2019-05
• 期刊: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Biqiao Zhang;S. Khorram;E. Provost

Cross-Corpus Acoustic Emotion Recognition with Multi-Task Learning: Seeking Common Ground While Preserving Differences

• DOI: 10.1109/taffc.2017.2684799
• 发表时间: 2019
• 期刊: IEEE Transactions on Affective Computing
• 影响因子: 11.2
• 作者: Biqiao Zhang;E. Provost;Georg Essl