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Pairwise Learning using Unsupervised Bottleneck Features for Zero-Resource Speech Challenge 2017 (System 1)

Yougen Yuan; Cheung-Chi Leung; Lei Xie; Hongjie Chen; Bin Ma; Haizhou Li


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  <identifier identifierType="DOI">10.5281/zenodo.814376</identifier>
  <creators>
    <creator>
      <creatorName>Yougen Yuan</creatorName>
      <affiliation>Northwestern Polytechnical University</affiliation>
    </creator>
    <creator>
      <creatorName>Cheung-Chi Leung</creatorName>
      <affiliation>Institute for Infocomm Research</affiliation>
    </creator>
    <creator>
      <creatorName>Lei Xie</creatorName>
      <affiliation>Northwestern Polytechnical University</affiliation>
    </creator>
    <creator>
      <creatorName>Hongjie Chen</creatorName>
      <affiliation>Northwestern Polytechnical University</affiliation>
    </creator>
    <creator>
      <creatorName>Bin Ma</creatorName>
      <affiliation>Institute for Infocomm Research</affiliation>
    </creator>
    <creator>
      <creatorName>Haizhou Li</creatorName>
      <affiliation>National University of Singapore</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Pairwise Learning using Unsupervised Bottleneck Features for Zero-Resource Speech Challenge 2017 (System 1)</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2017</publicationYear>
  <subjects>
    <subject>pairwise learning</subject>
    <subject>zero-resource</subject>
    <subject>unsupervised bottleneck features</subject>
    <subject>neural networks</subject>
    <subject>autoencoder</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2017-06-15</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/814376</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.809196</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/zerospeech2017</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://creativecommons.org/licenses/by-sa/4.0/legalcode">Creative Commons Attribution Share Alike 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The system is for track1 alone.  We trained an antoencoder using unsupervised bottleneck features with word-pair information from Switchboard. The unsupervised bottleneck features was extracted from an extractor of multi-task learning deep neural networks (MTL-DNN). The word-pair information was the ground truth from Switchboard. The final features are obtained from the third layer in our pairwise trained autoencoder.&lt;/p&gt;</description>
  </descriptions>
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