  Multi-modal conversation emotion recognition (MCER) aims to recognize and
track the speaker's emotional state using text, speech, and visual information
in the conversation scene. Analyzing and studying MCER issues is significant to
affective computing, intelligent recommendations, and human-computer
interaction fields. Unlike the traditional single-utterance multi-modal emotion
recognition or single-modal conversation emotion recognition, MCER is a more
challenging problem that needs to deal with more complex emotional interaction
relationships. The critical issue is learning consistency and complementary
semantics for multi-modal feature fusion based on emotional interaction
relationships. To solve this problem, people have conducted extensive research
on MCER based on deep learning technology, but there is still a lack of
systematic review of the modeling methods. Therefore, a timely and
comprehensive overview of MCER's recent advances in deep learning is of great
significance to academia and industry. In this survey, we provide a
comprehensive overview of MCER modeling methods and roughly divide MCER methods
into four categories, i.e., context-free modeling, sequential context modeling,
speaker-differentiated modeling, and speaker-relationship modeling. In
addition, we further discuss MCER's publicly available popular datasets,
multi-modal feature extraction methods, application areas, existing challenges,
and future development directions. We hope that our review can help MCER
researchers understand the current research status in emotion recognition,
provide some inspiration, and develop more efficient models.
