mlsanityData to 14 Oct 2026

Locality enhanced dynamic biasing and sampling strategies for contextual ASR

arXiv 2401.13146 ↗First posted 23 Jan 2024eess.AS cs.CL cs.SDcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 31 of its 39 references that are in the map. First posted on arXiv 23 Jan 2024.

Builds on

31 of 39 references.

References · 31

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19 more
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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2024Optimizing Contextual Speech Recognition Using Vector Quantization for Efficient Retrievalcited by 3
  2. 2025Enhancing the Robustness of Contextual ASR to Varying Biasing Information Volumes Through Purified Semantic Correlation Joint Modelingcited by 1
  3. 2025Retrieval Augmented Generation based context discovery for ASRcited by 1