Speaker Role Identification in Call Center Dialogues: Leveraging Opening Sentences and Large Language Models

Minh-Quoc Nghiem, Nichola Roberts, Dmitry Sityaev


In Sessions:

Sigdial Poster Session 2: (Thursday, 14:00 CEST, Foyer , Chat on Discord )

Abstract: This paper addresses the task of speaker role identification in call centre dialogues, focusing on distinguishing between the customer and the agent. We propose a text-based approach that utilises the identification of the agent's opening sentence as a key feature for role classification. The opening sentence is identified using a model trained through active learning. By combining this information with a large language model, we accurately classify the speaker roles. The proposed approach is evaluated on a dataset of call centre dialogues and achieves 93.61% accuracy. This work contributes to the field by providing an effective solution for speaker role identification in call centre settings, with potential applications in interaction analysis and information retrieval.