Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-Task Learning for End-to-End ASR Word and Utterance Confidence
  with Deletion Prediction   Confidence scores are very useful for downstream applications of automatic
speech recognition (ASR) systems. Recent works have proposed using neural
networks to learn word or utterance confidence scores for end-to-end ASR. In
those studies, word confidence by itself does not model deletions, and
utterance confidence does not take advantage of word-level training signals.
This paper proposes to jointly learn word confidence, word deletion, and
utterance confidence. Empirical results show that multi-task learning with all
three objectives improves confidence metrics (NCE, AUC, RMSE) without the need
for increasing the model size of the confidence estimation module. Using the
utterance-level confidence for rescoring also decreases the word error rates on
Google's Voice Search and Long-tail Maps datasets by 3-5% relative, without
needing a dedicated neural rescorer.

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The categories of this paper are: acc-phys
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The categories of this paper are: physics
31
The categories of this paper are: ao-sci
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
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The categories of this paper are: hep-lat
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The categories of this paper are: q-fin
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The categories of this paper are: chao-dyn
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: q-bio
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The categories of this paper are: math
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The categories of this paper are: math-ph