  Affect recognition, encompassing emotions, moods, and feelings, plays a
pivotal role in human communication. In the realm of conversational artificial
intelligence, the ability to discern and respond to human affective cues is a
critical factor for creating engaging and empathetic interactions. This study
investigates the capacity of large language models (LLMs) to recognise human
affect in conversations, with a focus on both open-domain chit-chat dialogues
and task-oriented dialogues. Leveraging three diverse datasets, namely IEMOCAP
(Busso et al., 2008), EmoWOZ (Feng et al., 2022), and DAIC-WOZ (Gratch et al.,
2014), covering a spectrum of dialogues from casual conversations to clinical
interviews, we evaluate and compare LLMs' performance in affect recognition.
Our investigation explores the zero-shot and few-shot capabilities of LLMs
through in-context learning as well as their model capacities through
task-specific fine-tuning. Additionally, this study takes into account the
potential impact of automatic speech recognition errors on LLM predictions.
With this work, we aim to shed light on the extent to which LLMs can replicate
human-like affect recognition capabilities in conversations.
