Effect and Analysis of Large-scale Language Model Rescoring on Competitive ASR Systems
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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Methods
Adam •
Attention Dropout •
BERT •
BPE •
Cosine Annealing •
Dense Connections •
Discriminative Fine-Tuning •
Dropout •
GELU •
GPT-2 •
Layer Normalization •
Linear Layer •
Linear Warmup With Cosine Annealing •
Linear Warmup With Linear Decay •
Multi-Head Attention •
Residual Connection •
RoBERTa •
Scaled Dot-Product Attention •
Softmax •
Weight Decay •
WordPiece
