TagSpeech: End-to-End Multi-Speaker ASR and Diarization with Fine-Grained Temporal Grounding

Jul 14, 2026

TagSpeech is a unified framework for joint multi-speaker automatic speech recognition (ASR) and diarization, leveraging large language models (LLMs) with a novel temporal anchor grounding mechanism. The approach uses decoupled semantic and speaker streams, interleaved with time anchors, to achieve fine-grained alignment of speaker identity and spoken content. Experiments on the AMI and AliMeeting benchmarks show that TagSpeech achieves consistent improvements in Diarization Error Rate over strong end-to-end baselines, including Qwen-Omni and Gemini, especially in challenging overlapping speech scenarios.

Why it matters: This work demonstrates a significant advance in multi-speaker speech processing by enabling explicit, fine-grained modeling of 'who spoke what and when' in an efficient end-to-end system.

Full story at: arXiv Audio and Speech Processing ↗