Papers by Junseong Lee
SpeakerSleuth: Can Large Audio-Language Models Judge Speaker Consistency across Multi-turn Dialogues? (2026.acl-long)
Copied to clipboard
| Challenge: | Large Audio-Language Models (LALMs) are a popular approach for evaluating speech quality, yet their ability to assess speaker consistency across multi-turn dialogues remains unexplored. |
| Approach: | They construct 1,818 human-verified evaluation instances across four datasets spanning synthetic and real speech, with controlled acoustic difficulty. |
| Outcome: | The proposed model performs better in comparing and ranking acoustic variants, demonstrating inherent acustic discrimination capabilities. |
Can Large Language Models be Effective Online Opinion Miners? (2025.emnlp-main)
Copied to clipboard
| Challenge: | OOMB is a novel benchmark designed to assess the ability of large language models (LLMs) to extract and analyze opinions from diverse and complex online environments. |
| Approach: | They propose an online opinion mining benchmark to assess the ability of large language models to extract and analyze opinions from diverse online environments. |
| Outcome: | The proposed benchmark assesses the ability of large language models to mine opinions effectively from diverse and complex online environments. |