Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Developing Real-time Streaming Transformer Transducer for Speech
  Recognition on Large-scale Dataset   Recently, Transformer based end-to-end models have achieved great success in
many areas including speech recognition. However, compared to LSTM models, the
heavy computational cost of the Transformer during inference is a key issue to
prevent their applications. In this work, we explored the potential of
Transformer Transducer (T-T) models for the fist pass decoding with low latency
and fast speed on a large-scale dataset. We combine the idea of Transformer-XL
and chunk-wise streaming processing to design a streamable Transformer
Transducer model. We demonstrate that T-T outperforms the hybrid model, RNN
Transducer (RNN-T), and streamable Transformer attention-based encoder-decoder
model in the streaming scenario. Furthermore, the runtime cost and latency can
be optimized with a relatively small look-ahead.

2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
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The categories of this paper are: hep-th
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The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas