Identifying Speakers in Dialogue Transcripts: A Text-based Approach Using Pretrained Language Models

Autor:	Nguyen, Minh, Dernoncourt, Franck, Yoon, Seunghyun, Deilamsalehy, Hanieh, Tan, Hao, Rossi, Ryan, Tran, Quan Hung, Bui, Trung, Nguyen, Thien Huu
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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} Comment: accepted to INTERSPEECH 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.12094 Zobrazit plný text záznamu View this record from Arxiv