  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.
