  The recurrent neural network transducer (RNN-T) is a prominent streaming
end-to-end (E2E) ASR technology. In RNN-T, the acoustic encoder commonly
consists of stacks of LSTMs. Very recently, as an alternative to LSTM layers,
the Conformer architecture was introduced where the encoder of RNN-T is
replaced with a modified Transformer encoder composed of convolutional layers
at the frontend and between attention layers. In this paper, we introduce a new
streaming ASR model, Convolutional Augmented Recurrent Neural Network
Transducers (ConvRNN-T) in which we augment the LSTM-based RNN-T with a novel
convolutional frontend consisting of local and global context CNN encoders.
ConvRNN-T takes advantage of causal 1-D convolutional layers,
squeeze-and-excitation, dilation, and residual blocks to provide both global
and local audio context representation to LSTM layers. We show ConvRNN-T
outperforms RNN-T, Conformer, and ContextNet on Librispeech and in-house data.
In addition, ConvRNN-T offers less computational complexity compared to
Conformer. ConvRNN-T's superior accuracy along with its low footprint make it a
promising candidate for on-device streaming ASR technologies.
