Speaker-Reasoner: Scaling Interaction Turns and Reasoning Patterns for Timestamped Speaker-Attributed ASR

Zhennan Lin; Shuai Wang; Zhaokai Sun; Pengyuan Xie; Chuan Xie; Jie Liu; Qiang Zhang; Lei Xie

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arXiv · 2604.03074

Speaker-Reasoner: Scaling Interaction Turns and Reasoning Patterns for Timestamped Speaker-Attributed ASR

Zhennan Lin·Shuai Wang·Zhaokai Sun·Pengyuan Xie·Chuan Xie·Jie Liu·Qiang Zhang·Lei Xie

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Abstract

Transcribing and understanding multi-speaker conversations requires speech recognition, speaker attribution, and timestamp localization. While speech LLMs excel at single-speaker tasks, multi-speaker scenarios remain challenging due to overlapping speech, backchannels, rapid turn-taking, and context window constraints. We propose Speaker-Reasoner, an end-to-end Speech LLM with agentic multi-turn temporal reasoning. Instead of single-pass inference, the model iteratively analyzes global audio structure, autonomously predicts temporal boundaries, and performs fine-grained segment analysis, jointly modeling speaker identity, gender, timestamps, and transcription. A speaker-aware cache further extends processing to audio exceeding the training context window. Trained with a three-stage progressive strategy, Speaker-Reasoner achieves consistent improvements over strong baselines on AliMeeting and AISHELL-4 datasets, particularly in handling overlapping speech and complex turn-taking.

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Zhennan Lin, Shuai Wang, Zhaokai Sun, Pengyuan Xie, Chuan Xie, Jie Liu, Qiang Zhang, Lei Xie. 2026-04-03. Speaker-Reasoner: Scaling Interaction Turns and Reasoning Patterns for Timestamped Speaker-Attributed ASR. https://arxiv.org/abs/2604.03074

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