Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Query-by-Example Search with Discriminative Neural Acoustic Word
  Embeddings   Query-by-example search often uses dynamic time warping (DTW) for comparing
queries and proposed matching segments. Recent work has shown that comparing
speech segments by representing them as fixed-dimensional vectors --- acoustic
word embeddings --- and measuring their vector distance (e.g., cosine distance)
can discriminate between words more accurately than DTW-based approaches. We
consider an approach to query-by-example search that embeds both the query and
database segments according to a neural model, followed by nearest-neighbor
search to find the matching segments. Earlier work on embedding-based
query-by-example, using template-based acoustic word embeddings, achieved
competitive performance. We find that our embeddings, based on recurrent neural
networks trained to optimize word discrimination, achieve substantial
improvements in performance and run-time efficiency over the previous
approaches.

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The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: bayes-an
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The categories of this paper are: eess
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The categories of this paper are: math-ph
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The categories of this paper are: q-bio
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The categories of this paper are: q-fin
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The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
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The categories of this paper are: physics
0
The categories of this paper are: hep-ph