Speaker Naming in TV programs Based on Speaker Role Recognition

Bellagha, Mohamed Lazhar; Zrigui, Mounir

doi:10.1109/aiccsa50499.2020.9316511

Cited by 8 publications

(1 citation statement)

References 28 publications

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“…Identifying Numerous studies have been undertaken to address the issue of speaker role identification. These efforts involve utilising text-based features (Barzilay et al, 2000;Liu, 2006;Wang et al, 2011;Sapru and Valente, 2012;Flemotomos et al, 2019), or employing multimodal approaches that integrate both text and audio features (Rouvier et al, 2015;Bellagha and Zrigui, 2020;Guo et al, 2023). In both cases, the goal is to classify each speaker in a conversation into a predefined role category.…”

Section: Introductionmentioning

confidence: 99%

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

Nghiem,

Roberts,

Sityaev

2023

Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue

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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.

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