基于似然比的E2E模型领域自适应方法
A Likelihood Ratio based Domain Adaptation Method for E2E Models
摘要：
端到端（E2 E）自动语音识别模型（如递归神经网络换能器（RNN-T））正在成为语音助理等流ASR应用的热门选择。虽然E2 E模型在学习它们所训练的训练数据的表示方面非常有效，但它们在看不见的域上的准确性仍然是一个具有挑战性的问题。此外，这些模型需要成对的音频和文本训练数据，计算成本高，并且难以适应会话语音的快速演变性质。在这项工作中，我们探索了一种使用似然比的上下文偏置方法，该方法利用文本数据源使RNN-T模型适应新的域和实体。我们表明，这种方法是有效的，在提高罕见的单词识别，并导致在1-最好的字出错率（WER）和10%的相对改善在n-best Oracle WER（n=8）在多个域外的数据集上没有任何退化的一般数据集。我们还表明，补充的上下文偏置自适应与适应的第二次通过重新评分模型提供了额外的WER改善。
英文摘要：
End-to-end (E2E) automatic speech recognition models like Recurrent Neural Networks Transducer (RNN-T) are becoming a popular choice for streaming ASR applications like voice assistants. While E2E models are very effective at learning representation of the training data they are trained on, their accuracy on unseen domains remains a challenging problem. Additionally, these models require paired audio and text training data, are computationally expensive and are difficult to adapt towards the fast evolving nature of conversational speech. In this work, we explore a contextual biasing approach using likelihood-ratio that leverages text data sources to adapt RNN-T model to new domains and entities. We show that this method is effective in improving rare words recognition, and results in a relative improvement of 10% in 1-best word error rate (WER) and 10% in n-best Oracle WER (n=8) on multiple out-of-domain datasets without any degradation on a general dataset. We also show that complementing the contextual biasing adaptation with adaptation of a second-pass rescoring model gives additive WER improvements.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
