Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Extremely Low Footprint End-to-End ASR System for Smart Device   Recently, end-to-end (E2E) speech recognition has become popular, since it
can integrate the acoustic, pronunciation and language models into a single
neural network, which outperforms conventional models. Among E2E approaches,
attention-based models, e.g. Transformer, have emerged as being superior. Such
models have opened the door to deployment of ASR on smart devices, however they
still suffer from requiring a large number of model parameters. We propose an
extremely low footprint E2E ASR system for smart devices, to achieve the goal
of satisfying resource constraints without sacrificing recognition accuracy. We
design cross-layer weight sharing to improve parameter efficiency and further
exploit model compression methods including sparsification and quantization, to
reduce memory storage and boost decoding efficiency. We evaluate our approaches
on the public AISHELL-1 and AISHELL-2 benchmarks. On the AISHELL-2 task, the
proposed method achieves more than 10x compression (model size reduces from 248
to 24MB), at the cost of only minor performance loss (CER reduces from 6.49% to
6.92%).

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The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
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The categories of this paper are: adap-org
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The categories of this paper are: hep-th
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The categories of this paper are: patt-sol
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The categories of this paper are: cmp-lg
2
The categories of this paper are: math