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arXiv · 2509.15612

Thinking in cocktail party: Chain-of-Thought and reinforcement learning for target speaker automatic speech recognition

Yiru Zhang·Hang Su·Lichun Fan·Zhenbo Luo·Jian Luan
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Abstract

Target Speaker Automatic Speech Recognition (TS-ASR) aims to transcribe the speech of a specified target speaker from multi-speaker mixtures in cocktail party scenarios. Recent advancement of Large Audio-Language Models (LALMs) has already brought some new insights to TS-ASR. However, significant room for optimization remains for the TS-ASR task within the LALMs architecture. While Chain of Thoughts (CoT) and Reinforcement Learning (RL) have proven effective in certain speech tasks, TS-ASR, which requires the model to deeply comprehend speech signals, differentiate various speakers, and handle overlapping utterances is particularly well-suited to a reasoning-guided approach. Therefore, we propose a novel framework that incorporates CoT and RL training into TS-ASR for performance improvement. A novel CoT dataset of TS-ASR is constructed, and the TS-ASR model is first trained on regular data and then fine-tuned on CoT data. Finally, the model is further trained with RL using selected data to enhance generalized reasoning capabilities. Experiment results show a significant improvement of TS-ASR performance with CoT and RL training, which demonstrates the effectiveness of the proposed CoT and RL training methods adapted for the TS-ASR task.

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Yiru Zhang, Hang Su, Lichun Fan, Zhenbo Luo, Jian Luan. 2025-09-19. Thinking in cocktail party: Chain-of-Thought and reinforcement learning for target speaker automatic speech recognition. https://arxiv.org/abs/2509.15612

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