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

帕金森病(PD)影响运动和认知能力，并导致沟通障碍，严重影响患者及其照顾者的生活质量。目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，然而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。有一个尚未得到满足的临床需求，即充分描述与帕金森病相关的沟通障碍的频谱，这些障碍干扰了日常生活的正常参与，并影响了生活质量。这将有助于根据准确的语言和交流特征(这也可以作为这些试验的结果衡量标准)选择患者参加治疗试验。这些临床生物标志物也可用于疾病状态的诊断和/或监测。到目前为止，关于帕金森病自然对话言语的研究还很少，尽管人们普遍认为对互动语境中的交际行为的研究对于我们理解认知交际障碍是至关重要的。不幸的是，人工分析自然环境中的自发语音通信在资源和研究人员时间方面构成了巨大的挑战。人工智能工具的开发将代表着言语治疗研究的重大进步，有可能转化为常规临床实践。目的本项目旨在探索自动标注和分析帕金森病患者自发语音的新方法，重点关注对话，以检测对话困难和可能用作数字生物标志物的语音特征。具体地说，该项目的目标是：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说话者的连接语音样本，以提取发声、语音转换、语言内容和副语言特征，用于机器学习建模，建立在我们实验室为AD2开发的方法的基础上；2.使用现有的数据集(如DementiaBank、意大利帕金森语音和语音数据库和MDVR-KCL数据集)为诊断、进展监测和分类的PD语音建模3.使用罗伯茨博士进行的治疗研究中收集的数据在对话中建模PD语音，该研究由10个二元组组成，每个二元组包括一名中度帕金森病患者及其配偶。对话数据(音频和视频)被记录在参与者的家中，作为基线治疗数据的一部分。在典型的家庭用餐时间，对话被记录在自然环境中，使用高保真记录设备，而没有研究人员在场。每个对话文件包含约60分钟的对话数据。将从苏格兰和加拿大的队列中收集新的数据，并将在这些数据上测试模型，以评估时间和韵律差异是否会影响分割和推断的准确性。模型将根据人工注释这一黄金标准进行评估。培训结果学生将接受多模式数据集分析方面的培训，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课程和通过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特定培训。

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.