Identifying Speakers in Dialogue Transcripts: A Text-based Approach Using Pretrained Language Models
<|reference_start|>Identifying Speakers in Dialogue Transcripts: A Text-based Approach Using Pretrained Language Models: We introduce an approach to identifying speaker names in dialogue transcripts, a crucial task for enhancing content accessibility and searchability in digital media archives. Despite the advancements in speech recognition, the task of text-based speaker identification (SpeakerID) has received limited attention, lacking large-scale, diverse datasets for effective model training. Addressing these gaps, we present a novel, large-scale dataset derived from the MediaSum corpus, encompassing transcripts from a wide range of media sources. We propose novel transformer-based models tailored for SpeakerID, leveraging contextual cues within dialogues to accurately attribute speaker names. Through extensive experiments, our best model achieves a great precision of 80.3\%, setting a new benchmark for SpeakerID. The data and code are publicly available here: \url{https://github.com/adobe-research/speaker-identification}<|reference_end|>
@article{nguyen2024identifying,
    title={Identifying Speakers in Dialogue Transcripts: A Text-based Approach
  Using Pretrained Language Models},
    author={Minh Nguyen, Franck Dernoncourt, Seunghyun Yoon, Hanieh Deilamsalehy,
  Hao Tan, Ryan Rossi, Quan Hung Tran, Trung Bui, Thien Huu Nguyen},
    journal={arXiv preprint arXiv:2407.12094},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12094},
    primaryClass={cs.CL}
}