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.

摘要 在美国，三分之一的老年人死于痴呆症，其中阿尔茨海默病（AD）是最常见的 form. AD患者遭受有意义的沟通和互动能力下降，这导致 对专业护理人员和家庭成员都有很大的压力和负担。社会辅助机器人 （SAR）旨在促进治疗互动和沟通。不幸的是， 智能（AI）长期以来一直受到老年人言语的挑战，老年人表现出与年龄相关的声音 震颤、犹豫、辅音产生不精确、基频变异性增加，以及 与AD相关的神经系统变化可能加剧的其他障碍，进一步复杂化 常见的环境噪声如吊扇、电视等，由于其产生的真实感较差 语音和语言的理解，稀缺的人类护理人员往往是 需要通过SAR相互作用指导AD患者，将SAR限制在小型部署，主要作为 调查研究。与现有的纯粹依赖人工智能的方法不同，care.coach™正在开发一种类似SAR的 通过真正自然的语音与老年人和AD患者交谈。每个化身都由一个 由训练有素的人员组成的24 x7团队，可以经济高效地监测和吸引12名或更多患者 通过来自患者的化身设备的音频/视觉馈送顺序地（2同时地）进行。工作人员 通过发送文本命令与每个患者进行通信， 语音合成引擎。工作人员为系统贡献了他们人类的语言和自然能力。 语言处理（NLP）和生成自由形式的会话响应，以帮助患者建立 与神通的私人关系工作人员由嵌入到系统中的软件驱动的专家系统指导。 他们的工作界面，这是编程与基于证据的提示和协议，以支持健康 行为和自我照顾。这个SBIR快速通道项目将利用我们的人类产生的独特数据- 在环平台开发新的ASR功能，实现全自动会话协议， 参与和支持AD患者，无需人为干预。我们在第一阶段的目标是利用我们独特的 工作数据集来训练自动语音识别（ASR）引擎，以使人们能够理解某些语音。 类型的老年人和AD患者的语音比任何目前可用的引擎更成功。我们瞄准 第二阶段是将这个新引擎沿着一个NLP模块整合到我们现有的人在环化身中 系统，招募AD患者群体以在2年的人类受试者期间进一步训练和验证 研究，以便我们可以展示我们的化身对话的重要部分的完全自动化， 中度AD患者。因此，我们将提高我们的化身的商业可扩展性，同时验证 我们的新ASR/NLP引擎是实现下一代以广告为重点的SAR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