Couples' relationships affect the physical health and emotional well-being of partners. Automatically recognizing each partner's emotions could give a better understanding of their individual emotional well-being, enable interventions and provide clinical benefits. In the paper, we summarize and synthesize works that have focused on developing and evaluating systems to automatically recognize the emotions of each partner based on couples' interaction or conversation contexts. We identified 28 articles from IEEE, ACM, Web of Science, and Google Scholar that were published between 2010 and 2021. We detail the datasets, features, algorithms, evaluation, and results of each work as well as present main themes. We also discuss current challenges, research gaps and propose future research directions. In summary, most works have used audio data collected from the lab with annotations done by external experts and used supervised machine learning approaches for binary classification of positive and negative affect. Performance results leave room for improvement with significant research gaps such as no recognition using data from daily life. This survey will enable new researchers to get an overview of this field and eventually enable the development of emotion recognition systems to inform interventions to improve the emotional well-being of couples.
翻译:夫妻关系会影响伴侣的身心健康和情感福祉。自动认识每个伴侣的情感可以更好地了解他们个人的情感福祉,促成干预和提供临床利益。在论文中,我们总结和综合了以开发和评估系统为重点的工作,以便根据夫妻互动或对话背景自动认识每个伴侣的情感。我们从2010年至2021年出版的IEEE、ACM、科学网和谷歌学者的28篇文章中找出了28篇文章。我们详细介绍了每个工作的数据集、特征、算法、评估、结果以及当前的主要专题。我们还讨论了当前的挑战、研究差距和提出未来的研究方向。总而言之,大多数工作都使用了实验室收集的带外部专家说明的音频数据,并使用了监督的机器学习方法对积极和消极影响进行二元分类。工作成绩留有改进的空间,如不使用日常生活数据进行识别等重大研究差距。这项调查将使新研究人员能够了解该领域的概况,并最终能够开发情感识别系统,为干预措施提供信息,以改善夫妇的情感幸福。