Analysis of Parkinson's speech patterns for digital biomarker discovery and intervention assessment

帕金森氏症言语模式分析，用于数字生物标志物发现和干预评估

• 批准号: 2887263
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2887263
• 关键词: Analysis; Parkinson; speech; patterns; digital

Parkinson's disease (PD) affects motor and cognitive abilities and leads to communication impairments that significantly impact on quality of life for patients and their caregivers. Current speech and language therapies rely on voice amplification training to improve voice clarity, however there are other conversation strategies that could benefit patients with different types of communication impairment. There is an unmet clinical need to fully characterise the spectrum of PD-related communication deficits that interfere with normal participation in everyday life and impact on quality of life. This would facilitate patient selection into treatment trials based on precise speech and communication characteristics (which could also be used as outcome measures for those trials). These clinical biomarkers could also be used in the diagnosis and/or monitoring of disease states. To date, little research has been done on natural dialogical speech in PD, despite the fact that it is widely acknowledged that the study of communicative behaviour in interactive contexts is crucial to our understanding of cognitive-communication disorders1. Unfortunately, manual analysis of spontaneous speech communication in natural environments poses significant challenges in terms of resources and researcher time. The development of AI tools would represent a significant advance in speech therapy research, with the potential for translation into routine clinical practice. Aims This project aims to investigate novel methods for automatic annotation and analysis of PD patients' spontaneous speech, with a focus on dialogue, for the detection of conversation difficulties and speech features that may be useful as digital biomarkers. Specifically, the project aims to: 1. analyse connected speech samples from participants at different stages of PD as well as control speakers, in controlled (reading) tasks and spontaneous dialogues to extract vocalisation, speech turns, linguistic content and paralinguistic features for machine learning modelling, building on methods developed in our lab for AD2; 2. model PD speech for diagnosis, progression monitoring and classification, using existing datasets (such as DementiaBank, Italian Parkinson's Voice and Speech Database, and the MDVR-KCL dataset) 3. model PD speech in dialogues using data collected as part of a treatment study conducted by Dr. Roberts, consisting of 10 dyads, each comprising a person with moderate stage PD and their spouse. Conversation data were recorded (audio and video) in participants' homes as part of the baseline treatment data. Conversations were recorded in a naturalistic environment during typical family mealtimes using high fidelity recording equipment without the presence of researchers. Each conversation file contains ~60 minutes of conversation data. New data will be collected from cohorts in Scotland and Canada, and models will be tested on these data to assess whether temporal and prosodic differences affect the accuracy of segmentation and inference. Models will be assessed against the gold standard of manual annotation. Training outcomes The student will receive training in analysis of multimodal data sets including speech data for the creation of clinically relevant models. This will be done via advanced signal processing and machine learning approaches, including speech segmentation and clustering methods, deep neural networks, and vocalisation modelling3. The student will receive general training in machine learning methodology through attendance in DTP courses and through Dr Luz's lab, and specific training on communication disorders.

帕金森病（PD）影响运动和认知能力，并导致沟通障碍，严重影响患者及其护理人员的生活质量。当前的言语和语言治疗依赖于声音放大训练来提高声音清晰度，但还有其他对话策略可以使患有不同类型沟通障碍的患者受益。目前尚未满足的临床需求是完全消除干扰正常参与日常生活并影响生活质量的PD相关沟通障碍。这将有助于根据精确的言语和沟通特征（也可用作这些试验的结果指标）选择患者进入治疗试验。这些临床生物标志物也可用于诊断和/或监测疾病状态。到目前为止，很少有研究已经做了自然对话语音PD，尽管人们普遍认为，在互动环境中的交际行为的研究是至关重要的，我们的认知沟通障碍的理解1。不幸的是，在自然环境中的自发语音通信的手动分析提出了重大的挑战，在资源和研究人员的时间。人工智能工具的开发将代表语言治疗研究的重大进展，并有可能转化为常规临床实践。本项目旨在研究自动注释和分析PD患者自发言语的新方法，重点是对话，用于检测可能用作数字生物标志物的对话困难和言语特征。具体而言，该项目旨在：1。在受控（阅读）任务和自发对话中，分析来自PD不同阶段的参与者以及控制扬声器的连接语音样本，以提取发声，语音转向，语言内容和机器学习建模的语言特征，建立在我们实验室为AD 2开发的方法基础上; 2.使用现有数据集（例如DementiaBank、意大利帕金森语音和语音数据库以及MDVR-KCL数据集）对PD语音进行建模，用于诊断、进展监测和分类。使用作为Roberts博士进行的治疗研究的一部分收集的数据，在对话中模拟PD语音，由10对二人组组成，每对二人组包括一名中度PD患者及其配偶。在参与者家中记录对话数据（音频和视频），作为基线治疗数据的一部分。在典型的家庭用餐时间，使用高保真录音设备在自然环境中记录了对话，没有研究人员在场。每个会话文件包含约60分钟的会话数据。将从苏格兰和加拿大的队列中收集新数据，并对这些数据进行模型测试，以评估时间和韵律差异是否会影响分割和推断的准确性。将根据手动注释的黄金标准对模型进行评估。培训结果学生将接受多模态数据集分析的培训，包括用于创建临床相关模型的语音数据。这将通过先进的信号处理和机器学习方法来完成，包括语音分割和聚类方法，深度神经网络和发声建模3。学生将通过参加DTP课程和Luz博士的实验室接受机器学习方法的一般培训，以及关于沟通障碍的具体培训。