Due to the mismatch between the source and target domains, how to better
utilize the biased word information to improve the performance of the automatic
speech recognition model in the target domain becomes a hot research topic.
Previous approaches either decode with a fixed external language model or
introduce a sizeable biasing module, which leads to poor adaptability and slow
inference. In this work, we propose CB-Conformer to improve biased word
recognition by introducing the Contextual Biasing Module and the Self-Adaptive
Language Model to vanilla Conformer. The Contextual Biasing Module combines
audio fragments and contextual information, with only 0.2% model parameters of
the original Conformer. The Self-Adaptive Language Model modifies the internal
weights of biased words based on their recall and precision, resulting in a
greater focus on biased words and more successful integration with the
automatic speech recognition model than the standard fixed language model. In
addition, we construct and release an open-source Mandarin biased-word dataset
based on WenetSpeech. Experiments indicate that our proposed method brings a
15.34% character error rate reduction, a 14.13% biased word recall increase,
and a 6.80% biased word F1-score increase compared with the base Conformer.

本文提出了 CB-Conformer 方法，将 Contextual Biasing Module 和自适应语言模型引入到 vanilla Conformer 中，用于改进偏见词识别。同时，作者还构建并公开了一个基于 WenetSpeech 的普通话偏见词数据集。实验证明，相比于基准 Conformer，我们的方法使字符错误率减少了 15.34％，偏见词召回率增加了 14.13％，偏见词 F1 分数提高了 6.80％。