Identifying Speakers in Dialogue Transcripts: A Text-based Approach Using Pretrained Language Models
This addresses the lack of large-scale datasets and effective models for speaker identification in digital media archives, enhancing accessibility and searchability, though it appears incremental in applying transformers to a specific domain.
The paper tackles the problem of identifying speaker names in dialogue transcripts by introducing a novel transformer-based approach and a large-scale dataset derived from MediaSum, achieving 80.3% precision and setting a new benchmark for text-based speaker identification.
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}