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

只要听父母谈话五分钟，就可以揭示出关于孩子未来精神病理学的大量信息。对父母讲话中使用的词语和语气的分析，提供了关于父母，孩子和家庭内部互动的详细情况。一种被称为五分钟演讲样本（FMSS）的采访技术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，并帮助描述他们发展和康复的风险，以及从精神健康障碍中恢复过来。FMSS很容易收集-你只需要5分钟和一个口述录音机/智能手机-但它们很少用于研究或临床环境，因为语音编码是费力的，容易产生偏见，需要训练有素的评分员。如果这些问题可以克服，FMSS提供了巨大的机会，用于跨研究，心理健康和社会护理设置，以快速评估青少年心理健康问题的关键可修改的驱动因素。该项目将汇集一个跨学科的发展精神病理学家团队，创意作家，加上计算机，临床和社会科学家，以自动化编码的FMSS。利用计算语言学和情感计算的最新发展，我们将创建一个结合自动翻译，语音效价和自然语言分析的管道。我们将使用来自英国E-Risk纵向双胞胎研究的研究人员评定的母亲FMSS音频记录的独特集合，这些音频记录是在2031名10岁儿童中获得的。该队列中的儿童被随访至18岁，接受了多波全面评估。在现有可行性研究的基础上，我们将开发和培训一种自动化的FMSS编码方法-利用样本的规模和社会经济代表性。然后，我们将研究从10岁母亲的语音样本中生成的自动评级是否显示出同样的能力来预测12岁和18岁的心理健康问题，以及昂贵的人类评级。我们将在整个项目中与关键利益相关者（年轻人，父母，医疗保健/社会工作从业者，研究决策者，治理领导和教育工作者）进行创造性的研讨会，以确定使用和共享父母语音数据的主要道德，社会和实际挑战，并在实践中开发和实施自动化模型，以告知未来的工作。至关重要的是，所有这些工作将帮助我们了解如何分享这种方法，作为最终成果，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具有成本效益的方式快速编码FMSS的表达情感。

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.