  Recurrent neural network transducer (RNN-T) is an end-to-end speech
recognition framework converting input acoustic frames into a character
sequence. The state-of-the-art encoder network for RNN-T is the Conformer,
which can effectively model the local-global context information via its
convolution and self-attention layers. Although Conformer RNN-T has shown
outstanding performance, most studies have been verified in the setting where
the train and test data are drawn from the same domain. The domain mismatch
problem for Conformer RNN-T has not been intensively investigated yet, which is
an important issue for the product-level speech recognition system. In this
study, we identified that fully connected self-attention layers in the
Conformer caused high deletion errors, specifically in the long-form out-domain
utterances. To address this problem, we introduce sparse self-attention layers
for Conformer-based encoder networks, which can exploit local and generalized
global information by pruning most of the in-domain fitted global connections.
Also, we propose a state reset method for the generalization of the prediction
network to cope with long-form utterances. Applying proposed methods to an
out-domain test, we obtained 27.6% relative character error rate (CER)
reduction compared to the fully connected self-attention layer-based
Conformers.
