The attention-based deep contextual biasing method has been demonstrated to effectively improve the recognition performance of end-to-end automatic speech recognition (ASR) systems on given contextual phrases. However, unlike shallow fusion methods that directly bias the posterior of the ASR model, deep biasing methods implicitly integrate contextual information, making it challenging to control the degree of bias. In this study, we introduce a spike-triggered deep biasing method that simultaneously supports both explicit and implicit bias. Moreover, both bias approaches exhibit significant improvements and can be cascaded with shallow fusion methods for better results. Furthermore, we propose a context sampling enhancement strategy and improve the contextual phrase filtering algorithm. Experiments on the public WenetSpeech Mandarin biased-word dataset show a 32.0% relative CER reduction compared to the baseline model, with an impressively 68.6% relative CER reduction on contextual phrases.

翻译：基于注意力的深度上下文偏置方法已被证明能有效提升端到端自动语音识别（ASR）系统在给定上下文短语上的识别性能。然而，与通过直接偏置ASR模型后验概率的浅融合方法不同，深度偏置方法隐式融合上下文信息，导致偏置程度难以控制。本研究提出一种基于脉冲触发的深度偏置方法，可同时支持显式与隐式偏置。两种偏置方法均展现出显著改进，并能与浅融合方法级联以获得更优结果。此外，我们提出上下文采样增强策略，并改进了上下文短语过滤算法。在公开的WenetSpeech中文偏置词语料库上的实验表明，相较于基线模型，字符错误率（CER）相对降低32.0%，其中上下文短语的CER相对降低幅度高达68.6%。