论文标题
对竞争性ASR系统的大规模语言模型的效果和分析
Effect and Analysis of Large-scale Language Model Rescoring on Competitive ASR Systems
论文作者
论文摘要
大规模的语言模型（LLM），例如GPT-2，BERT和ROBERTA已成功应用于ASR N-OX-t-t-t-t-t-t-t-tesk撤退。但是，在最新的ASR系统附近，它们是否或如何使竞争性受益。在这项研究中，我们将LLM撤销纳入最具竞争力的ASR基准之一：构象异构体模型。我们证明，LLM的双向，预处理，内域填充和上下文增强可以实现一致的改进。此外，我们的词汇分析阐明了这些组件中的每一个如何有助于ASR性能。
Large-scale language models (LLMs) such as GPT-2, BERT and RoBERTa have been successfully applied to ASR N-best rescoring. However, whether or how they can benefit competitive, near state-of-the-art ASR systems remains unexplored. In this study, we incorporate LLM rescoring into one of the most competitive ASR baselines: the Conformer-Transducer model. We demonstrate that consistent improvement is achieved by the LLM's bidirectionality, pretraining, in-domain finetuning and context augmentation. Furthermore, our lexical analysis sheds light on how each of these components may be contributing to the ASR performance.
