  Transformer has achieved competitive performance against state-of-the-art
end-to-end models in automatic speech recognition (ASR), and requires
significantly less training time than RNN-based models. The original
Transformer, with encoder-decoder architecture, is only suitable for offline
ASR. It relies on an attention mechanism to learn alignments, and encodes input
audio bidirectionally. The high computation cost of Transformer decoding also
limits its use in production streaming systems. To make Transformer suitable
for streaming ASR, we explore Transducer framework as a streamable way to learn
alignments. For audio encoding, we apply unidirectional Transformer with
interleaved convolution layers. The interleaved convolution layers are used for
modeling future context which is important to performance. To reduce
computation cost, we gradually downsample acoustic input, also with the
interleaved convolution layers. Moreover, we limit the length of history
context in self-attention to maintain constant computation cost for each
decoding step. We show that this architecture, named Conv-Transformer
Transducer, achieves competitive performance on LibriSpeech dataset (3.6\% WER
on test-clean) without external language models. The performance is comparable
to previously published streamable Transformer Transducer and strong hybrid
streaming ASR systems, and is achieved with smaller look-ahead window (140~ms),
fewer parameters and lower frame rate.
