Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Integrating Categorical Features in End-to-End ASR   All-neural, end-to-end ASR systems gained rapid interest from the speech
recognition community. Such systems convert speech input to text units using a
single trainable neural network model. E2E models require large amounts of
paired speech text data that is expensive to obtain. The amount of data
available varies across different languages and dialects. It is critical to
make use of all these data so that both low resource languages and high
resource languages can be improved. When we want to deploy an ASR system for a
new application domain, the amount of domain specific training data is very
limited. To be able to leverage data from existing domains is important for ASR
accuracy in the new domain. In this paper, we treat all these aspects as
categorical information in an ASR system, and propose a simple yet effective
way to integrate categorical features into E2E model. We perform detailed
analysis on various training strategies, and find that building a joint model
that includes categorical features can be more accurate than multiple
independently trained models.

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The categories of this paper are: cmp-lg
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The categories of this paper are: chao-dyn
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The categories of this paper are: funct-an
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The categories of this paper are: q-bio
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The categories of this paper are: nlin
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: stat
0
The categories of this paper are: hep-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: math-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: adap-org
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The categories of this paper are: econ
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-th