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Automatic Speech Recognition (ASR) still face challenges when recognizingtime-variant rare-phrases. Contextual biasing (CB) modules bias ASR modeltowards such contextually-relevant phrases. During training, a list of biasingphrases are selected from a large pool of phrases following a samplingstrategy. In this work we firstly analyse different sampling strategies toprovide insights into the training of CB for ASR with correlation plots betweenthe bias embeddings among various training stages. Secondly, we introduce aneighbourhood attention (NA) that localizes self attention (SA) to the nearestneighbouring frames to further refine the CB output. The results show that thisproposed approach provides on average a 25.84% relative WER improvement onLibriSpeech sets and rare-word evaluation compared to the baseline.

Locality enhanced dynamic biasing and sampling strategies for contextual ASR