Cummins Nicholas, White Lauren Louise, Rahman Zahia, Lucas Catriona, Pan Tian, Carr Ewan, Matcham Faith, Downs Johnny, Dobson Richard, Quatieri Thomas F, Dineley Judith
Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
School of Psychology, University of Sussex, Brighton, United Kingdom.
JMIR Res Protoc. 2025 Jul 22;14:e69431. doi: 10.2196/69431.
Speech and language biomarkers have the potential to provide regular, objective assessments of symptom severity in several neurological and mental health conditions, both in the clinic and remotely. However, speech and language characteristics within an individual are influenced by multiple variables that can make findings highly dependent on the chosen methodology and study cohort. These characteristics are often not reported adequately in studies investigating speech-based health assessment, which (1) hinders the progress of methodological speech research, (2) prevents replication, and (3) makes the definitive identification of robust biomarkers problematic.
This study aims (1) to facilitate replicable speech research by presenting a transparent speech collection and feature extraction protocol and design checklist for other researchers to adapt and design for their own experiments and (2) to demonstrate in a pilot study the feasibility of implementing our example in-laboratory protocol that reduces multiple potential confounding factors in repeated recordings of healthy speech.
We developed a collection and feature extraction protocol based on a thematic literature review to enable a controlled investigation of within-individual speech variability in healthy individuals. Our protocol comprises the elicitation of read speech, held vowels, and a picture description and extraction of 14 example features relevant to health. We collected speech using a freestanding condenser microphone, 3 smartphones, and a headset to enable a sensitivity analysis across different recording devices.
We collected healthy speech data from 28 individuals 3 times in 1 day (the "day" cohort), with the same schedule repeated 8 to 11 weeks later, and from 25 individuals on 3 days within 1 week at fixed times (the "week" cohort). Participant characteristics collected included sex, age, native language, and voice use habits. Before each recording, we collected information on recent voice use, food and drink intake, and emotional state. Recording times were also documented. Analysis relating to exploring within-individual variability within the day and week cohorts, as well as the device-type sensitivity analysis, is ongoing, with findings expected later in 2025.
The wide variability in speech data collection, processing, analysis, and reporting in research on speech's use in clinical trials and practice is the motivation for this paper and the development of the speech curation protocol design checklist. Increased, more consistent reporting and justification of study protocols is urgently required to facilitate speech research replication and translation into clinical practice.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/69431.
言语和语言生物标志物有潜力在临床及远程环境下，为多种神经和精神健康状况下的症状严重程度提供定期、客观的评估。然而，个体内部的言语和语言特征会受到多个变量的影响，这可能导致研究结果高度依赖所选的方法和研究队列。在基于言语的健康评估研究中，这些特征往往没有得到充分报告，这（1）阻碍了言语方法学研究的进展，（2）妨碍了研究的重复验证，（3）使得确定可靠的生物标志物变得困难。
本研究旨在（1）通过提供一个透明的言语收集和特征提取方案以及设计清单，便于其他研究人员根据自身实验进行调整和设计，以促进可重复的言语研究；（2）在一项试点研究中展示实施我们的实验室示例方案的可行性，该方案可减少健康言语重复记录中的多种潜在混杂因素。
我们基于主题文献综述制定了一个收集和特征提取方案，以便对健康个体内部的言语变异性进行可控研究。我们的方案包括朗读言语、持续元音和图片描述的引出，以及提取14个与健康相关的示例特征。我们使用独立电容式麦克风、3部智能手机和一副耳机收集言语，以便对不同记录设备进行敏感性分析。
我们在一天内从28名个体收集了3次健康言语数据（“日”队列），8至11周后重复相同的时间表；并在一周内的3天固定时间从25名个体收集了数据（“周”队列）。收集的参与者特征包括性别、年龄、母语和语音使用习惯。每次记录前，我们收集了近期语音使用、饮食摄入和情绪状态的信息。记录时间也有记录。关于探索日队列和周队列中个体内部变异性以及设备类型敏感性分析的研究正在进行中，预计2025年晚些时候得出结果。
言语在临床试验和实践中的研究在数据收集、处理、分析和报告方面存在广泛差异，这是本文以及言语整理方案设计清单得以制定的动机。迫切需要增加对研究方案更一致的报告和论证，以促进言语研究的重复验证并转化为临床实践。
国际注册报告识别号（IRRID）：DERR1-10.2196/69431。
