Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to retrieve out-of-vocabulary words in speech recognition   Many Proper Names (PNs) are Out-Of-Vocabulary (OOV) words for speech
recognition systems used to process diachronic audio data. To help recovery of
the PNs missed by the system, relevant OOV PNs can be retrieved out of the many
OOVs by exploiting semantic context of the spoken content. In this paper, we
propose two neural network models targeted to retrieve OOV PNs relevant to an
audio document: (a) Document level Continuous Bag of Words (D-CBOW), (b)
Document level Continuous Bag of Weighted Words (D-CBOW2). Both these models
take document words as input and learn with an objective to maximise the
retrieval of co-occurring OOV PNs. With the D-CBOW2 model we propose a new
approach in which the input embedding layer is augmented with a context anchor
layer. This layer learns to assign importance to input words and has the
ability to capture (task specific) key-words in a bag-of-word neural network
model. With experiments on French broadcast news videos we show that these two
models outperform the baseline methods based on raw embeddings from LDA,
Skip-gram and Paragraph Vectors. Combining the D-CBOW and D-CBOW2 models gives
faster convergence during training.

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The categories of this paper are: math-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas
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The categories of this paper are: chem-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: cmp-lg
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The categories of this paper are: gr-qc
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The categories of this paper are: q-alg
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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: adap-org
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The categories of this paper are: ao-sci
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The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
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The categories of this paper are: math