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4. An Empirical Study Of Transformer-Based Neural Language Model Adaptation

An Empirical Study Of Transformer-Based Neural Language Model Adaptation

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IEEE ICASSP 2020 Virtual Conference May 2020

We explore two adaptation approaches of deep Transformer based neural language models (LMs) for automatic speech recognition. The first approach is a pretrain-finetune framework, where we first pretrain a Transformer LM on a large-scale text corpus from s

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An Empirical Study Of Transformer-Based Neural Language Model Adaptation

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We explore two adaptation approaches of deep Transformer based neural language models (LMs) for automatic speech recognition. The first approach is a pretrain-finetune framework, where we first pretrain a Transformer LM on a large-scale text corpus from s