Exploring emotional climate recognition in peer conversations through bispectral features and affect dynamics

Abstract

The findings highlight the effectiveness of MLBispec in objectively recognizing peers' EC during conversations, setting a new standard for practical emotionally-aware applications. These include point-of-care healthcare, human-computer interfaces (HCI), and large-language models (LLMs). By enabling dynamic and reliable EC recognition, MLBispec paves the way for advancements in emotionally intelligent systems.