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arXiv · 2204.00212

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

Takuma Udagawa·Masayuki Suzuki·Gakuto Kurata·Nobuyasu Itoh·George Saon
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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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Takuma Udagawa, Masayuki Suzuki, Gakuto Kurata, Nobuyasu Itoh, George Saon. 2022-04-01. Effect and Analysis of Large-scale Language Model Rescoring on Competitive ASR Systems. https://arxiv.org/abs/2204.00212

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