End-to-end (E2E) automatic speech recognition (ASR) methods exhibit
remarkable performance. However, since the performance of such methods is
intrinsically linked to the context present in the training data, E2E-ASR
methods do not perform as desired for unseen user contexts (e.g., technical
terms, personal names, and playlists). Thus, E2E-ASR methods must be easily
contextualized by the user or developer. This paper proposes an attention-based
contextual biasing method that can be customized using an editable phrase list
(referred to as a bias list). The proposed method can be trained effectively by
combining a bias phrase index loss and special tokens to detect the bias
phrases in the input speech data. In addition, to improve the contextualization
performance during inference further, we propose a bias phrase boosted (BPB)
beam search algorithm based on the bias phrase index probability. Experimental
results demonstrate that the proposed method consistently improves the word
error rate and the character error rate of the target phrases in the bias list
on both the Librispeech-960 (English) and our in-house (Japanese) dataset,
respectively.

该论文提出了一种基于注意力的上下文偏置方法，利用可编辑的短语列表（称为偏置列表）来实现用户或开发人员的定制化，该方法通过组合偏置短语索引损失和特殊标记来有效地训练，以检测输入语音数据中的偏置短语。此外，为了进一步提高推理过程中的上下文化性能，我们提出了一种基于偏置短语索引概率的偏置短语增强（BPB）束搜索算法。实验结果表明，所提出的方法在 Librispeech-960（英文）和我们的内部（日文）数据集上一致改善了目标短语偏置列表的词错误率和字符错误率。