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

Cross-utterance ASR Rescoring with Graph-based Label Propagation

Part-of-Speech (POS) tagging is an important component of the NLP pipeline,
but many low-resource languages lack labeled data for training. An established
method for training a POS tagger in such a scenario is to create a labeled
training set by transferring from high-resource languages. In this paper, we
propose a novel method for transferring labels from multiple high-resource
source to low-resource target languages. We formalize POS tag projection as
graph-based label propagation. Given translations of a sentence in multiple
languages, we create a graph with words as nodes and alignment links as edges
by aligning words for all language pairs. We then propagate node labels from
source to target using a Graph Neural Network augmented with transformer
layers. We show that our propagation creates training sets that allow us to
train POS taggers for a diverse set of languages. When combined with enhanced
contextualized embeddings, our method achieves a new state-of-the-art for
unsupervised POS tagging of low-resource languages.

本文提出了一种将标签从多种高 - 低资源语言进行传递的图形标签传递方法，可与增强的情境化嵌入相结合，实现了无监督标注低资源语言中词性标注的最新技术水平。