  In the Emotion Recognition in Conversation task, recent investigations have
utilized attention mechanisms exploring relationships among utterances from
intra- and inter-speakers for modeling emotional interaction between them.
However, attributes such as speaker personality traits remain unexplored and
present challenges in terms of their applicability to other tasks or
compatibility with diverse model architectures. Therefore, this work introduces
a novel framework named BiosERC, which investigates speaker characteristics in
a conversation. By employing Large Language Models (LLMs), we extract the
"biographical information" of the speaker within a conversation as
supplementary knowledge injected into the model to classify emotional labels
for each utterance. Our proposed method achieved state-of-the-art (SOTA)
results on three famous benchmark datasets: IEMOCAP, MELD, and EmoryNLP,
demonstrating the effectiveness and generalization of our model and showcasing
its potential for adaptation to various conversation analysis tasks. Our source
code is available at https://github.com/yingjie7/BiosERC.
