Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2326-2329. doi: 10.1109/EMBC46164.2021.9630833.
The COVID-19 pandemic has led to unprecedented restrictions in people's lifestyle which have affected their psychological wellbeing. In this context, this paper investigates the use of social signal processing techniques for remote assessment of emotions. It presents a machine learning method for affect recognition applied to recordings taken during the COVID-19 winter lockdown in Scotland (UK). This method is exclusively based on acoustic features extracted from voice recordings collected through home and mobile devices (i.e. phones, tablets), thus providing insight into the feasibility of monitoring people's psychological wellbeing remotely, automatically and at scale. The proposed model is able to predict affect with a concordance correlation coefficient of 0.4230 (using Random Forest) and 0.3354 (using Decision Trees) for arousal and valence respectively.Clinical relevance- In 2018/2019, 12% and 14% of Scottish adults reported depression and anxiety symptoms. Remote emotion recognition through home devices would support the detection of these difficulties, which are often underdiagnosed and, if untreated, may lead to temporal or chronic disability.
新冠疫情大流行导致人们的生活方式受到前所未有的限制，这影响了他们的心理健康。在这种情况下，本文研究了使用社会信号处理技术进行远程情感评估。本文提出了一种应用于苏格兰（英国）新冠冬季封锁期间录制的声音记录的情感识别机器学习方法。该方法完全基于从家庭和移动设备（如电话、平板电脑）采集的语音记录中提取的声学特征，从而深入了解远程、自动和大规模监测人们心理健康的可行性。该模型能够分别以 0.4230（使用随机森林）和 0.3354（使用决策树）的一致性相关系数预测唤醒度和效价。临床意义-在 2018/2019 年，12%和 14%的苏格兰成年人报告了抑郁和焦虑症状。通过家庭设备进行远程情感识别将有助于检测这些困难，这些困难往往诊断不足，如果得不到治疗，可能会导致暂时或长期残疾。
