Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Acoustic Word Embeddings with Temporal Context for
  Query-by-Example Speech Search   We propose to learn acoustic word embeddings with temporal context for
query-by-example (QbE) speech search. The temporal context includes the leading
and trailing word sequences of a word. We assume that there exist spoken word
pairs in the training database. We pad the word pairs with their original
temporal context to form fixed-length speech segment pairs. We obtain the
acoustic word embeddings through a deep convolutional neural network (CNN)
which is trained on the speech segment pairs with a triplet loss. Shifting a
fixed-length analysis window through the search content, we obtain a running
sequence of embeddings. In this way, searching for the spoken query is
equivalent to the matching of acoustic word embeddings. The experiments show
that our proposed acoustic word embeddings learned with temporal context are
effective in QbE speech search. They outperform the state-of-the-art
frame-level feature representations and reduce run-time computation since no
dynamic time warping is required in QbE speech search. We also find that it is
important to have sufficient speech segment pairs to train the deep CNN for
effective acoustic word embeddings.

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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: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: eess
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-bio
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: physics
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The categories of this paper are: nucl-th
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The categories of this paper are: math
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The categories of this paper are: mtrl-th
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The categories of this paper are: dg-ga