Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: HuBERT-EE: Early Exiting HuBERT for Efficient Speech Recognition   Pre-training with self-supervised models, such as Hidden-unit BERT (HuBERT)
and wav2vec 2.0, has brought significant improvements in automatic speech
recognition (ASR). However, these models usually require an expensive
computational cost to achieve outstanding performance, slowing down the
inference speed. To improve the model efficiency, we propose an early exit
scheme for ASR, namely HuBERT-EE, that allows the model to stop the inference
dynamically. In HuBERT-EE, multiple early exit branches are added at the
intermediate layers, and each branch is used to decide whether a prediction can
be exited early. Experimental results on the LibriSpeech dataset show that
HuBERT-EE can accelerate the inference of a large-scale HuBERT model while
simultaneously balancing the trade-off between the word error rate (WER)
performance and the latency.

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The categories of this paper are: nlin
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
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The categories of this paper are: dg-ga
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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The categories of this paper are: atom-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: math
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The categories of this paper are: acc-phys
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The categories of this paper are: physics
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The categories of this paper are: q-alg
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The categories of this paper are: quant-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: cmp-lg