End-to-End (E2E) automatic speech recognition (ASR) systems used in voice assistants often have difficulties recognizing infrequent words personalized to the user, such as names and places. Rare words often have non-trivial pronunciations, and in such cases, human knowledge in the form of a pronunciation lexicon can be useful. We propose a PROnunCiation-aware conTextual adaptER (PROCTER) that dynamically injects lexicon knowledge into an RNN-T model by adding a phonemic embedding along with a textual embedding. The experimental results show that the proposed PROCTER architecture outperforms the baseline RNN-T model by improving the word error rate (WER) by 44% and 57% when measured on personalized entities and personalized rare entities, respectively, while increasing the model size (number of trainable parameters) by only 1%. Furthermore, when evaluated in a zero-shot setting to recognize personalized device names, we observe 7% WER improvement with PROCTER, as compared to only 1% WER improvement with text-only contextual attention

翻译：E2E自动语音识别（ASR）系统通常难以识别个性化的罕见词汇，例如名字和地名。罕见词汇通常有非常规的发音，在这种情况下，具有发音词典的人类知识可能是有用的。我们提出了一种名为"PROCTER"的发音感知上下文适应引擎，通过添加音素嵌入和文本嵌入将词典知识动态注入RNN-T模型。实验结果表明，与基线RNN-T模型相比，所提出的PROCTER架构在个性化实体及个性化罕见实体上通过将模型大小（可训练参数数量）仅增加1％实现了44％和57％的单词错误率（WER）的改进。此外，当在零-shot设置中进行评估以识别个性化设备名称时，我们观察到使用PROCTER的7％ WER 改进，而仅使用文本上下文注意力可实现1％ WER 改进。