Automatic Speech Recognition (ASR) still face challenges when recognizing
time-variant rare-phrases. Contextual biasing (CB) modules bias ASR model
towards such contextually-relevant phrases. During training, a list of biasing
phrases are selected from a large pool of phrases following a sampling
strategy. In this work we firstly analyse different sampling strategies to
provide insights into the training of CB for ASR with correlation plots between
the bias embeddings among various training stages. Secondly, we introduce a
neighbourhood attention (NA) that localizes self attention (SA) to the nearest
neighbouring frames to further refine the CB output. The results show that this
proposed approach provides on average a 25.84% relative WER improvement on
LibriSpeech sets and rare-word evaluation compared to the baseline.

通过分析不同的采样策略和相关性图，本文首先对上下文偏置模块的训练进行了探究。其次，引入了邻居注意力机制来进一步优化上下文偏置的输出，实验结果表明相对于基准模型，在 LibriSpeech 数据集和稀有单词评估上平均相对词错误率提升了 25.84%。