  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.
