We propose a novel approach for ASR N-best hypothesis rescoring with graph-based label propagation by leveraging cross-utterance acoustic similarity. In contrast to conventional neural language model (LM) based ASR rescoring/reranking models, our approach focuses on acoustic information and conducts the rescoring collaboratively among utterances, instead of individually. Experiments on the VCTK dataset demonstrate that our approach consistently improves ASR performance, as well as fairness across speaker groups with different accents. Our approach provides a low-cost solution for mitigating the majoritarian bias of ASR systems, without the need to train new domain- or accent-specific models.

翻译：我们提出了一种基于图标签传播的跨语句ASR N-best假设重新评分新方法，通过利用语句间的声学相似性实现。与传统的基于神经语言模型的ASR重新评分/排序模型不同，我们的方法聚焦于声学信息，并以协同方式而非独立方式对语句进行重新评分。在VCTK数据集上的实验表明，我们的方法持续提升了ASR性能，并增强了不同口音说话人群体之间的公平性。该方法无需训练新的领域或口音专用模型，即可低成本缓解ASR系统的主流偏见问题。