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.

本文提出了一种基于图形标签传播的新方法，通过利用跨话语声学相似性重新评分ASR N个最佳假设。与传统的基于神经LM的ASR重新评分/重新排名模型不同，我们的方法依 focuses on acoustic information，并在话语之间协作地进行退避，而不是单独进行。在VCTK数据集上的实验表明，我们的方法始终提高ASR性能，以及不同口音的讲话者组之间的公平性。我们的方法提供了一种低成本的解决方案，可减轻ASR系统的多数派偏见，而无需训练新的特定领域或口音的模型。

基于图传播的跨句子ASR重新评分