Papers by Yiwen Shao
TagSpeech: End-to-End Multi-Speaker ASR and Diarization with Fine-Grained Temporal Grounding (2026.acl-long)
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| Challenge: | a critical ambiguity persists regarding what constitutes "joint ASR and diarization" a unified framework for multi-speaker ASR is proposed, but it is not yet clear what constitute "diarization." |
| Approach: | They propose a unified LLM-based framework that uses Temporal Anchor Grounding for joint multi-speaker ASR and diarization. |
| Outcome: | The proposed framework improves on AMI and AliMeeting benchmarks on speaker-content alignment . the proposed framework achieves consistent improvements in Diarization Error Rate over strong baselines . |
Revisiting Audio-language Pretraining for Learning General-purpose Audio Representation (2026.acl-long)
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| Challenge: | Existing methods for learning general-purpose audio representations are limited in scope and coverage of audio attributes. |
| Approach: | They propose to use a 10.7M caption dataset to compare ALP with captioning . they find that contrastive learning yields competitive, transferable representations . |
| Outcome: | The proposed model yields competitive, transferable representations, while captioning exhibits better scalability. |