Recently, Transformers have shown competitive automatic speech recognition (ASR) results . One key factor to the success of these models is the multi-head attention mechanism. However, we observed in trained models, the diagonal attention matrices indicating the redundancy of the corresponding attention heads. Furthermore, we found some architectures with reduced numbers of attention heads have better performance. Since the search for the best structure is time prohibitive, we propose to randomly remove attention heads during training and keep all attention heads at test time, thus the final model can be viewed as an average of models with different architectures. This method gives consistent performance gains  on the Wall Street Journal, AISHELL, Switchboard and AMI ASR tasks. On the AISHELL dev/test sets, the proposed method achieves state-of-the-art Transformer results with 5.8\%/6.3\% word error rates .
Recently, Transformer based models have shown competitive automatic speech recognition (ASR) performance . One key factor in the success of these models is the multi-head attention mechanism. However, for trained models, we have previously observed that many attention matrices are close to diagonal, indicating the redundancy of the corresponding attention heads. We have also found that some architectures with reduced numbers of attention heads have better performance. Since the search for the best structure is time prohibitive, we propose to randomly remove attention heads during training and keep all attention heads at test time, thus the final model is an ensemble of models with different architectures. The proposed method also forces each head independently learn the most useful patterns. We apply the proposed method to train Transformer based and Convolution-augmented Transformer (Conformer) based ASR models. Our method gives consistent performance gains over strong baselines on the Wall Street Journal, AISHELL, Switchboard and AMI datasets. To the best of our knowledge, we have achieved state-of-the-art end-to-end Transformer based model performance on Switchboard and AMI .
R
Transformers
Transformer based models
R
results
performance
R
to
in
R
we observed in
for
R
the diagonal attention matrices
we have previously observed that many attention matrices are close to diagonal,
R
Furthermore, we found
We have also found that
R
can be viewed as an average
is an ensemble
R
This method
The proposed method also forces each head independently learn the most useful patterns. We apply the proposed method to train Transformer based and Convolution-augmented Transformer (Conformer) based ASR models. Our method
A
over strong baselines
R
ASR tasks. On the AISHELL dev/test sets, the proposed method achieves
datasets. To the best of our knowledge, we have achieved
R
Transformer results with 5.8\%/6.3\% word error rates
end-to-end Transformer based model performance on Switchboard and AMI