The automated coding of expressed emotion to enhance clinical and epidemiological mental health research in adolescence

只需五分钟，听父母的谈话就能透露出有关孩子未来精神病态的大量信息。分析父母讲话中使用的词语和语气，提供关于父母、孩子和家庭内互动的详细图景。一种名为五分钟演讲样本(FMSS)的采访技巧已经将这一过程付诸实施。有很好的证据表明，FMSS可以提供儿童家庭环境的指数，并帮助描述他们患上青春期精神健康障碍的风险，以及从青春期开始的精神健康障碍中恢复的风险。FMS很容易收集--你只需要5分钟和一部录音机/智能手机--但它们很少在研究或临床环境中使用，因为语音编码繁琐，容易产生偏见，并且需要训练有素的评分员。如果能够克服这些问题，FMSS提供了巨大的机会，可以在研究、心理健康和社会护理环境中使用，以快速评估青少年心理健康问题的关键可改变的驱动因素。这个项目将汇集一个由发展精神病理学家、创造性作家以及计算机、临床和社会科学家组成的跨学科团队，以自动化FMSS的编码。利用计算语言学和情感计算的最新发展，我们将创建一条结合自动翻译、语音配价和自然语言分析的管道。我们将使用来自英国E-Risk纵向双胞胎研究的独特的研究人员评级的FMSS母亲录音集合，这些录音是在2031名10岁儿童身上获得的。该队列中的儿童一直被跟踪到18岁，经历了多次全面评估。在现有可行性研究的基础上，我们将开发和培训FMSS编码的自动化方法-利用样本的大小和社会经济代表性。然后，我们将检查10岁母亲语音样本生成的自动评分是否显示出与昂贵的人类评分相同的预测12岁和18岁心理健康问题的能力。我们将在整个项目期间与主要利益攸关方(年轻人、家长、医疗保健/社会工作从业者、研究政策制定者、治理领导和教育工作者)举办创造性研讨会，以确定在使用和共享父母语音数据以及开发和实施实践中的自动化模型以指导未来工作方面的主要伦理、社会和实践挑战。至关重要的是，所有这些工作将帮助我们理解如何分享这一方法，作为最终输出，我们将开发开源材料和构建安全数字平台所需的清晰蓝图，使其他研究小组能够以准确和经济高效的方式快速编码来自FMS的情感表达。

As little as five minutes listening to a parent talk can reveal a significant amount of information about their child's future psychopathology. Analyses of the words and tone used in parents' speech, provides a detailed picture about the parents, the child, and interactions within the family. An interview technique called the five-minute speech sample (FMSS) has operationalised this process. There is good evidence the FMSS can provide an index of a child's home environment and help profile their risk of developing, and recovering from, adolescent-onset mental health disorders. FMSS are easy to collect - all you need is 5 minutes and a dictaphone/smartphone - yet they are rarely used in research or clinical settings, because the coding of speech is laborious, bias-prone, and requires highly trained raters. If these issues could be overcome, FMSS presents tremendous opportunity to be used across research, mental health and social care settings to rapidly assess key modifiable drivers of mental health problems among adolescents.This project will bring together an interdisciplinary team of developmental psychopathologists, creative writers, plus computer, clinical and social scientists to automate the coding of the FMSS. Using recent developments in computational linguistics and affective computing, we will create a pipeline which combines automatic translation, speech valance and natural language analysis. We will use a unique collection of researcher-rated FMSS audio recordings of mothers from the UK E-Risk Longitudinal Twin Study, which were obtained on 2031 children at 10 years of age. The children in this cohort have been followed to age 18 years, undergoing multiple waves of comprehensive assessments. Building on an existing feasibility study, we will develop and train an automated approach to FMSS coding - exploiting both the size and socio-economic representativeness of the sample. We will then examine whether the automated ratings generated from age-10 maternal speech samples show the same ability to predict mental health problems at 12 and 18 years as well as costly human ratings. We will conduct creative workshops with key stakeholders (young people, parents, healthcare/social-work practitioners, research policymakers, governance leads, and educators) throughout the project to determine the main ethical, social and practical challenges to using and sharing parental speech data and developing and implementing the automated models in practice to inform future work. Crucially, all this work will help us understand how to share this methodology, and as a final output we will develop open-source materials and a clear blueprint of what is required to build a secure digital platform that enables other research groups to rapidly code expression emotion from FMSS in an accurate and cost-effective manner.