Speaker-attributed automatic speech recognition (SA-ASR) aims to transcribe speech while assigning transcripts to the corresponding speakers accurately. Existing methods often rely on complex modular systems or require extensive fine-tuning of joint modules, limiting their adaptability and general efficiency. This paper introduces a novel approach, leveraging a frozen multilingual ASR model to incorporate speaker attribution into the transcriptions, using only standard monolingual ASR datasets. Our method involves training a speaker module to predict speaker embeddings based on weak labels without requiring additional ASR model modifications. Despite being trained exclusively with non-overlapping monolingual data, our approach effectively extracts speaker attributes across diverse multilingual datasets, including those with overlapping speech. Experimental results demonstrate competitive performance compared to strong baselines, highlighting the model's robustness and potential for practical applications.

本研究解决了现有说话人归属自动语音识别（SA-ASR）方法的复杂性和适应性不足的问题。提出了一种新颖的方法，利用冻结的多语种ASR模型，通过弱标签预测说话人嵌入，进行说话人归属，且无需对ASR模型进行额外修改。实验结果表明，该方法在多语种数据集上表现出强大的竞争力，具有良好的实际应用潜力。

MSA-ASR：利用冻结的ASR模型进行高效多语种说话人归属