Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Performance of End-to-End ASR on Numeric Sequences   Recognizing written domain numeric utterances (e.g. I need $1.25.) can be
challenging for ASR systems, particularly when numeric sequences are not seen
during training. This out-of-vocabulary (OOV) issue is addressed in
conventional ASR systems by training part of the model on spoken domain
utterances (e.g. I need one dollar and twenty five cents.), for which numeric
sequences are composed of in-vocabulary numbers, and then using an FST
verbalizer to denormalize the result. Unfortunately, conventional ASR models
are not suitable for the low memory setting of on-device speech recognition.
E2E models such as RNN-T are attractive for on-device ASR, as they fold the AM,
PM and LM of a conventional model into one neural network. However, in the
on-device setting the large memory footprint of an FST denormer makes spoken
domain training more difficult. In this paper, we investigate techniques to
improve E2E model performance on numeric data. We find that using a
text-to-speech system to generate additional numeric training data, as well as
using a small-footprint neural network to perform spoken-to-written domain
denorming, yields improvement in several numeric classes. In the case of the
longest numeric sequences, we see reduction of WER by up to a factor of 8.

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The categories of this paper are: plasm-ph
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The categories of this paper are: nucl-ex
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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: hep-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: mtrl-th
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The categories of this paper are: funct-an
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: math-ph
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The categories of this paper are: physics
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The categories of this paper are: nlin
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The categories of this paper are: q-bio
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The categories of this paper are: adap-org