Recent advances in deep learning and automatic speech recognition have
improved the accuracy of end-to-end speech recognition systems, but recognition
of personal content such as contact names remains a challenge. In this work, we
describe our personalization solution for an end-to-end speech recognition
system based on connectionist temporal classification. Building on previous
work, we present a novel method for generating additional subword tokenizations
for personal entities from their pronunciations. We show that using this
technique in combination with two established techniques, contextual biasing
and wordpiece prior normalization, we are able to achieve personal named entity
accuracy on par with a competitive hybrid system.

使用深度学习和自动语音识别相结合的端到端语音识别系统在识别个人内容（如联系人姓名）方面的准确率有所提高，但仍存在挑战。本研究描述了我们基于连接主义时序分类的端到端语音识别系统的个性化解决方案，通过建立在以往研究的基础上，提出了一种从发音生成个人实体的附加子词划分的新方法。我们证明，在结合了上下文偏置和词片段先验归一化这两个已有技术的基础上，我们能够获得与具备竞争力的混合系统相当的个人命名实体准确性。