Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Investigation of Monotonic Transducers for Large-Scale Automatic
  Speech Recognition   The two most popular loss functions for streaming end-to-end automatic speech
recognition (ASR) are the RNN-Transducer (RNN-T) and the connectionist temporal
classification (CTC) objectives. Both perform an alignment-free training by
marginalizing over all possible alignments, but use different transition rules.
Between these two loss types we can classify the monotonic RNN-T (MonoRNN-T)
and the recently proposed CTC-like Transducer (CTC-T), which both can be
realized using the graph temporal classification-transducer (GTC-T) loss
function. Monotonic transducers have a few advantages. First, RNN-T can suffer
from runaway hallucination, where a model keeps emitting non-blank symbols
without advancing in time, often in an infinite loop. Secondly, monotonic
transducers consume exactly one model score per time step and are therefore
more compatible and unifiable with traditional FST-based hybrid ASR decoders.
However, the MonoRNN-T so far has been found to have worse accuracy than RNN-T.
It does not have to be that way, though: By regularizing the training - via
joint LAS training or parameter initialization from RNN-T - both MonoRNN-T and
CTC-T perform as well - or better - than RNN-T. This is demonstrated for
LibriSpeech and for a large-scale in-house data set.

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The categories of this paper are: nucl-ex
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The categories of this paper are: plasm-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: acc-phys
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The categories of this paper are: quant-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-th
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The categories of this paper are: gr-qc
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The categories of this paper are: dg-ga
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The categories of this paper are: q-bio
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The categories of this paper are: supr-con
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The categories of this paper are: nlin