Effect and Analysis of Large-scale Language Model Rescoring on Competitive ASR Systems

• URL: http://arxiv.org/abs/2204.00212v1
• Date: Fri, 1 Apr 2022 05:20:55 GMT
• Title: Effect and Analysis of Large-scale Language Model Rescoring on Competitive ASR Systems
• Authors: Takuma Udagawa, Masayuki Suzuki, Gakuto Kurata, Nobuyasu Itoh, George Saon
• Abstract summary: Large-scale language models (LLMs) have been successfully applied to ASR N-best rescoring. In this study, we incorporate LLM rescoring into one of the most competitive ASR baselines: the Conformer-Transducer model.
• Score: 30.873546090458678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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