  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.
