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- W4387156794 abstract "We propose a neural language modeling system based on low-rank adaptation (LoRA) for speech recognition output rescoring. Although pretrained language models (LMs) like BERT have shown superior performance in second-pass rescoring, the high computational cost of scaling up the pretraining stage and adapting the pretrained models to specific domains limit their practical use in rescoring. Here we present a method based on low-rank decomposition to train a rescoring BERT model and adapt it to new domains using only a fraction (0.08%) of the pretrained parameters. These inserted matrices are optimized through a discriminative training objective along with a correlation-based regularization loss. The proposed low-rank adaptation Rescore-BERT (LoRB) architecture is evaluated on LibriSpeech and internal datasets with decreased training times by factors between 5.4 and 3.6." @default.
- W4387156794 created "2023-09-30" @default.
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- W4387156794 date "2023-09-26" @default.
- W4387156794 modified "2023-10-12" @default.
- W4387156794 title "Low-rank Adaptation of Large Language Model Rescoring for Parameter-Efficient Speech Recognition" @default.
- W4387156794 doi "https://doi.org/10.48550/arxiv.2309.15223" @default.
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