End-to-end (E2E) speech recognition architectures assemble all components of
traditional speech recognition system into a single model. Although it
simplifies ASR system, it introduces contextual ASR drawback: the E2E model has
worse performance on utterances containing infrequent proper nouns. In this
work, we propose to add a contextual bias attention (CBA) module to attention
based encoder decoder (AED) model to improve its ability of recognizing the
contextual phrases. Specifically, CBA utilizes the context vector of source
attention in decoder to attend to a specific bias embedding. Jointly learned
with the basic AED parameters, CBA can tell the model when and where to bias
its output probability distribution. At inference stage, a list of bias phrases
is preloaded and we adapt the posterior distributions of both CTC and attention
decoder according to the attended bias phrase of CBA. We evaluate the proposed
method on GigaSpeech and achieve a consistent relative improvement on recall
rate of bias phrases ranging from 15% to 28% compared to the baseline model.
Meanwhile, our method shows a strong anti-bias ability as the performance on
general tests only degrades 1.7% even 2,000 bias phrases are present.

该研究针对 End-to-end 语音识别模型在识别罕见专有名词上性能较差的问题，提出了一种基于上下文偏置注意力的改进方案。该方案通过在注意力编码器解码器模型中添加上下文偏置注意力模块，使模型有能力识别上下文短语。在 GigaSpeech 数据集上的实验结果表明，该方法相比基线模型，提高了 15％至 28％的偏置短语召回率，并且拥有较强的反偏置能力。