A Specialized Automatic Speech Recognition and Conversational Platform to Enable Socially Assistive Robots for Persons with Mild-to-Moderate Alzheimer's Disease and Related Dementia

专门的自动语音识别和对话平台，为患有轻度至中度阿尔茨海默病和相关痴呆症的人提供社交辅助机器人

• 批准号: 10263325
• 负责人: Victor Wang
• 金额: $ 138.67万
• 依托单位: CARE.COACH CORPORATION
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10263325
• 关键词: Age; Alzheimer' s Disease; Alzheimer' s disease patient; Alzheimer' s disease related dementia; Artificial Intelligence; Automation; Behavior; Caregivers; Caring; Clinical Research; Communication; Community Hospitals; Computer software; Computers; Contracts; Data; Data Set; Delirium; Dementia; Dependence; Devices; Disease; Elderly; Exhibits; Expert Systems; Family; Family member; Feeds; Frequencies; Generations; Genetic Transcription; Goals; Home; Hospitals; Hour; Human; Hybrids; Individual; Institutes; Intervention; Jamaica; Label; Language; Licensing; Loneliness; Manuals; Measures; Medical center; Modeling; Monitor; Natural Language Processing; Network-based; Neural Network Simulation; Neurologic; Noise; Patients; Persons; Phase; Population; Price; Production; Protocols documentation; Reaction; Self Care; Semantics; Small Business Innovation Research Grant; Social Interaction; Social support; Speech; Stress; Study Subject; System; Techniques; Technology; Television; Text; Therapeutic; Training; Tremor; United States; Universities; Validation; Visual; Voice; Washington; Work; World Health Organization; age related; aging population; automated speech recognition; care providers; cost; deep neural network; depressive symptoms; design; evidence base; falls; health plan; human subject; human-in-the-loop; improved; next generation; older patient; patient engagement; patient response; phrases; recruit; research study; response; restraint; satisfaction; skills; social assistive robot; speech recognition; speech synthesis; success; usability

Abstract 1 in 3 seniors in the United States dies with dementia, of which Alzheimer’s disease (AD) is the most common form. AD patients suffer from decreased ability to meaningfully communicate and interact, which causes significant stress and burden for both professional caregivers and family members. Socially assistive robots (SARs) have been designed to promote therapeutic interaction and communication. Unfortunately, artificial intelligence (AI) has long been challenged by the speech of elderly persons, who exhibit age-related voice tremors, hesitations, imprecise production of consonants, increased variability of fundamental frequency, and other barriers that can be exacerbated by the neurological changes associated with AD, further complicated by common environmental noises such as the ceiling fan, television, etc. Because of the resulting poor real-world speech and language understanding by available SAR technologies, scarce human caregivers are often required to guide AD patients through SAR interactions, limiting SARs to small deployments, mostly as part of research studies. Unlike existing approaches relying purely on AI, care.coach™ is developing a SAR-like avatar that converses with elderly and AD patients through truly natural speech. Each avatar is controlled by a 24x7 team of trained human staff who can cost-effectively monitor and engage 12 or more patients sequentially (2 simultaneously) through the audio/visual feeds from the patient’s avatar device. The staff communicate with each patient by sending text commands which are converted into the avatar’s voice through a speech synthesis engine. The staff contribute to the system their human abilities for speech and natural language processing (NLP) and for generating free-form conversational responses to help patients build personal relationships with the avatar. The staff are guided by a software-driven expert system embedded into their work interface, which is programmed with evidence-based prompting and protocols to support healthy behaviors and self-care. This SBIR Fast-Track project will leverage the unique data generated by our human- in-the-loop platform to develop new ASR capabilities, enabling fully automatic conversational protocols to engage and support AD patients without human intervention. We aim in Phase I to leverage our unique prior work dataset to train an automatic speech recognition (ASR) engine to enable the understanding of certain types of elderly and AD patient speech more successfully than any currently available engine. We aim in Phase II to incorporate this new engine along with an NLP module into our existing human-in-the-loop avatar system, recruiting a population of AD patients to further train and validate with during a 2-year human subjects study so that we can demonstrate full automation of a significant portion of our avatar conversations with mild- to-moderate level AD patients. Thus, we will improve the commercial scalability of our avatars, while validating our new ASR/NLP engine as the most accurate platform for enabling the next generation of AD-focused SARs.

抽象的 美国三分之一的老年人死于痴呆症，其中阿尔茨海默氏病（AD）是最常见的 形式。广告患者的沟通能力降低，这导致 专业护理人员和家庭成员都有重大压力和燃烧。社会辅助机器人 （SARS）旨在促进治疗相互作用和沟通。不幸的是，人造 智力（AI）长期以来一直受到老年人的讲话的挑战，他们暴露了与年龄有关的声音 震颤，犹豫，暗示辅音的产生，基本频率的变异性增加以及 与AD相关的神经系统变化可能会加剧的其他障碍，这使得 由于造成的真实世界，吊扇，电视等常见的环境噪音，例如吊扇，电视等 可用的SAR技术的语音和语言理解，稀缺的人类护理人员通常是 需要引导广告患者进行SAR互动，将SARS限制为小部署，主要是 研究。与纯粹依靠AI的现有方法不同，Care.coach™正在开发类似SAR的样子 通过真正的自然语音与老年和AD患者交谈的头像。每个化身都由 24x7训练有素的人类员工团队，他们可以成本效益监控和聘请12名或更多患者 通过患者的头像设备的音频/视觉提要顺序（非常简单）（非常简单）。工作人员 通过发送文本命令通过转换为阿凡达的声音，与每个患者进行通信 语音合成引擎。员工为系统的言语和自然能力做出了贡献 语言处理（NLP）并产生自由形式的对话反应以帮助患者建立 与化身的个人关系。工作人员以软件驱动的专家系统为指导 他们的工作界面通过基于证据的提示和协议来支持健康 行为和自我保健。这个SBIR快速轨道项目将利用我们的人类产生的独特数据 开发新的ASR功能的环境平台，使完全自动对话协议能够 参与和支持AD患者无人干预。我们的目标是第一阶段来利用我们独特的先验 工作数据集以训练自动语音识别（ASR）引擎以使某些理解 与当前可用的任何引擎相比，老年和广告患者的演讲类型更成功。我们的目标 第二阶段将这款新引擎以及NLP模块加入我们现有的人类化身 系统，招募一组AD患者，以进一步训练并在2年的人类受试者中进行验证 研究以便我们可以证明我们的头像对话中很大一部分与轻度对话的自动化 适度的AD患者。这是，我们将在验证时提高化身的商业可扩展性 我们的新的ASR/NLP引擎是实现下一代主体SARS的最准确平台。

项目成果

Something Related to Education May Hold the Key to Understanding What Is Ailing the United States.

与教育相关的事情可能是理解美国问题的关键。

• DOI: 10.2105/ajph.2023.307375
• 发表时间: 2023
• 期刊: American journal of public health
• 影响因子: 12.7
• 作者: Case,Anne