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IMPROVING CTC-BASED SPEECH RECOGNITION VIA KNOWLEDGE TRANSFERRING FROM PRE-TRAINED LANGUAGE MODELS

Keqi Deng, Gaofeng Cheng, Ji Xu, Pengyuan Zhang, Songjun Cao, Yike Zhang, Long Ma

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    Length: 00:08:23
13 May 2022

Recently, end-to-end automatic speech recognition models based on connectionist temporal classification (CTC) have achieved impressive results, especially when fine-tuned from wav2vec2.0 models. Due to the conditional independence assumption, CTC-based models are always weaker than attention-based encoder-decoder models and require the assistance of external language models (LMs). To solve this issue, we propose two knowledge transferring methods that leverage pre-trained LMs, such as BERT and GPT2, to improve CTC-based models. The first method is based on representation learning, in which the CTC-based models use the representation produced by BERT as an auxiliary learning target. The second method is based on joint classification learning, which combines GPT2 for text modeling with a hybrid CTC/attention architecture. Experiment on AISHELL-1 corpus yields a character error rate (CER) of 4.2% on the test set. When compared to the vanilla CTC-based models fine-tuned from the wav2vec2.0 models, our knowledge transferring method reduces CER by 16.1% relatively without external LMs.

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    Members: Free
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    Members: Free
    IEEE Members: $11.00
    Non-members: $15.00