As voice assistants become more ubiquitous, they are increasingly expected to support and perform well on a wide variety of use-cases across different domains. We present a domain-aware rescoring framework suitable for achieving domain-adaptation during second-pass rescoring in production settings. In our framework, we fine-tune a domain-general neural language model on several domains, and use an LSTM-based domain classification model to select the appropriate domain-adapted model to use for second-pass rescoring. This domain-aware rescoring improves the word error rate by up to 2.4% and slot word error rate by up to 4.1% on three individual domains -- shopping, navigation, and music -- compared to domain general rescoring. These improvements are obtained while maintaining accuracy for the general use case.

中文翻译：

---

用于语音识别的领域感知神经语言模型

随着语音助手的普及，人们越来越希望它们在不同领域的各种用例上提供支持并表现良好。我们提出了一种域感知的评分框架，该框架适用于在生产环境中进行第二遍评分期间实现域自适应。在我们的框架中，我们在多个域上微调了一个领域通用的神经语言模型，并使用基于LSTM的领域分类模型来选择适当的领域自适应模型以用于第二遍记录。与域一般记录相比，此域感知记录可在三个单独的域（购物，导航和音乐）上将单词错误率提高多达2.4％，并将插槽单词错误率提高多达4.1％。在保持常规用例准确性的同时获得了这些改进。