Identifying Speakers in Dialogue Transcripts: A Text-based Approach Using Pretrained Language Models
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910530692710400 |
|---|---|
| author | Nguyen, Minh Dernoncourt, Franck Yoon, Seunghyun Deilamsalehy, Hanieh Tan, Hao Rossi, Ryan Tran, Quan Hung Bui, Trung Nguyen, Thien Huu |
| author_facet | Nguyen, Minh Dernoncourt, Franck Yoon, Seunghyun Deilamsalehy, Hanieh Tan, Hao Rossi, Ryan Tran, Quan Hung Bui, Trung Nguyen, Thien Huu |
| contents | 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} |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12094 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Identifying Speakers in Dialogue Transcripts: A Text-based Approach Using Pretrained Language Models Nguyen, Minh Dernoncourt, Franck Yoon, Seunghyun Deilamsalehy, Hanieh Tan, Hao Rossi, Ryan Tran, Quan Hung Bui, Trung Nguyen, Thien Huu Computation and Language 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} |
| title | Identifying Speakers in Dialogue Transcripts: A Text-based Approach Using Pretrained Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2407.12094 |