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.

翻译：在对话情感识别任务中，近期研究利用注意力机制探索说话人内部和说话人之间话语的关系，以建模其情感交互。然而，诸如说话人个性特质等属性仍未得到探索，并且其在其他任务中的适用性或与不同模型架构的兼容性方面存在挑战。因此，本研究提出了一种名为BiosERC的新颖框架，用于探究对话中的说话人特征。通过使用大语言模型，我们提取对话中说话人的“传记信息”作为补充知识注入模型，以对每个话语的情感标签进行分类。我们提出的方法在三个著名基准数据集：IEMOCAP、MELD和EmoryNLP上取得了最先进的结果，证明了我们模型的有效性和泛化能力，并展示了其适应各种对话分析任务的潜力。我们的源代码可在 https://github.com/yingjie7/BiosERC 获取。