Speaker Role Identification in Call Centre Dialogues: Leveraging Opening Sentences and Large Language Models (2023)

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.

## introduction speaker role identification is a fundamental process that involves recognizing different speaker roles in a conversation. its significance has grown in various settings, such as call centres, where distinguishing between agents and customers in a call transcript is critical. speaker role identification has numerous uses, including interaction and dialogue analysis, summarisation, and information retrieval @xcite . this paper concentrates exclusively on speaker role identification in call centre conversations. in our application, speaker role identification takes place after speaker diarisation, where speaker turn information has been added to the transcripts. within a call centre dialogue, two specific roles are present: the customer and the agent. while a typical call involves a single customer and a single agent, it is common for calls to involve more than one customer (as in numerous studies have been undertaken to address the issue of speaker role identification. these efforts involve utilising text-based features @xcite @xcite @xcite , or employing multimodal approaches that integrate both text and audio features @xcite @xcite . in both cases, the goal is to classify each speaker in a conversation into a predefined role category. this classification task is typically accomplished using machine learning algorithms that are trained on labelled datasets, which consist of conversations where each speaker is annotated with their corresponding role category. this paper focuses on the text-based approach and formulate the task as a binary classification problem, with the categories being "customer" and "agent". in call centre dialogues, distinguishing between the agent and the customer can be achieved by exploiting the language differences between them. the call centre agent typically starts the conversation by introducing themselves as a representative of their company or organization, which is referred to as the "opening sentence". we propose utilising the identification results of the opening sentence to identify the speaker roles. by combining this information with a large language model, we can accurately classify the speaker roles in call centres. this paper makes the following two key contributions: the remainder of this paper is organised as follows. section 2 provides a brief overview of the related work. section 3 presents details of our methodology. section 4 describes the experimental results and discussions. section 5 concludes the paper and points to avenues for future work. ## related work text-based speaker role identification often takes the form of a text classification task, aiming to categorise each speaker in a conversation into predefined role categories. traditionally, text classification has been accomplished using machine learning algorithms trained on labelled datasets. however, with the advent of the transformer neural network @xcite , many studies have adopted pre-trained large language models for text classification @xcite @xcite . fine-tuning these pretrained models still requires a certain amount of labelled data. active learning provides a means to quickly build labelled data by involving the model in the data labelling process @xcite . on the other hand, zero-shot text classification is an approach that requires no labelled data at all @xcite . in this method, a model is trained on a set of existing labelled examples and can subsequently classify new examples from previously unseen classes. this offers the advantage of categorising text into arbitrary categories without the requirement of data preprocessing and training. bart @xcite , bloom @xcite , and flan-t5 @xcite are notable pre-trained large language models available for research purposes, offering the ability to perform zero-shot learning. ## conclusion this paper proposes a text-based approach for speaker role identification in call centre dialogues. by combining the identification of the agent's opening sentence with a large language model, our approach achieves high accuracy in classifying speaker roles. this has practical implications for call centre applications, enabling improved customer-agent interaction analysis and call pattern analysis. the use of active learning allows for efficient construction of the training dataset for opening sentence identification. integrating this information into the classification process significantly improves the accuracy of speaker role identification. future work can explore enhancements to the system, such as incorporating additional contextual features and exploring multimodal approaches. evaluating the approach on larger and more diverse datasets would also provide a better understanding of its generalisability.