Log In Sign Up

Distilling the Knowledge of BERT for Sequence-to-Sequence ASR

by   Hayato Futami, et al.

Attention-based sequence-to-sequence (seq2seq) models have achieved promising results in automatic speech recognition (ASR). However, as these models decode in a left-to-right way, they do not have access to context on the right. We leverage both left and right context by applying BERT as an external language model to seq2seq ASR through knowledge distillation. In our proposed method, BERT generates soft labels to guide the training of seq2seq ASR. Furthermore, we leverage context beyond the current utterance as input to BERT. Experimental evaluations show that our method significantly improves the ASR performance from the seq2seq baseline on the Corpus of Spontaneous Japanese (CSJ). Knowledge distillation from BERT outperforms that from a transformer LM that only looks at left context. We also show the effectiveness of leveraging context beyond the current utterance. Our method outperforms other LM application approaches such as n-best rescoring and shallow fusion, while it does not require extra inference cost.


page 1

page 2

page 3

page 4


Distilling the Knowledge of BERT for CTC-based ASR

Connectionist temporal classification (CTC) -based models are attractive...

Hierarchical Transformer-based Large-Context End-to-end ASR with Large-Context Knowledge Distillation

We present a novel large-context end-to-end automatic speech recognition...

Integrating Whole Context to Sequence-to-sequence Speech Recognition

Because an attention based sequence-to-sequence speech (Seq2Seq) recogni...

Knowledge Distillation from BERT Transformer to Speech Transformer for Intent Classification

End-to-end intent classification using speech has numerous advantages co...

Transformer with Bidirectional Decoder for Speech Recognition

Attention-based models have made tremendous progress on end-to-end autom...

Guiding CTC Posterior Spike Timings for Improved Posterior Fusion and Knowledge Distillation

Conventional automatic speech recognition (ASR) systems trained from fra...