Recognition of personalized content remains a challenge in end-to-end speech
recognition. We explore three novel approaches that use personalized content in
a neural rescoring step to improve recognition: gazetteers, prompting, and a
cross-attention based encoder-decoder model. We use internal de-identified
en-US data from interactions with a virtual voice assistant supplemented with
personalized named entities to compare these approaches. On a test set with
personalized named entities, we show that each of these approaches improves
word error rate by over 10%, against a neural rescoring baseline. We also show
that on this test set, natural language prompts can improve word error rate by
7% without any training and with a marginal loss in generalization. Overall,
gazetteers were found to perform the best with a 10% improvement in word error
rate (WER), while also improving WER on a general test set by 1%.

研究三种新方法，使用个性化内容在神经重新评分步骤中提高识别率：地名词典，提示和基于交叉注意力的编码器 - 解码器模型。使用虚拟语音助手交互的内部去识别化的 en-US 数据和个性化的命名实体来比较这些方法，结果显示地名词典具有最优效果，词误率（WER）提高了 10％，同时在一般测试集上提高了 1％。