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Multilingual bottle-neck feature learning from untranscribed speech for track 1 in zerospeech2017 (system 2 -- with VTLN)

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


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  <identifier identifierType="DOI">10.5281/zenodo.822737</identifier>
  <creators>
    <creator>
      <creatorName>Hongjie Chen Chen</creatorName>
      <affiliation>Northwestern Polytechnical University</affiliation>
    </creator>
    <creator>
      <creatorName>Cheung-Chi Leung</creatorName>
      <affiliation>Institute for Infocomm Research, A*STAR</affiliation>
    </creator>
    <creator>
      <creatorName>Lei Xie</creatorName>
      <affiliation>Northwestern Polytechnical University</affiliation>
    </creator>
    <creator>
      <creatorName>Bin Ma</creatorName>
      <affiliation>Institute for Infocomm Research, A*STAR</affiliation>
    </creator>
    <creator>
      <creatorName>Haizhou Li</creatorName>
      <affiliation>National University of Singapore</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Multilingual bottle-neck feature learning from untranscribed speech for track 1 in zerospeech2017 (system 2 -- with VTLN)</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2017</publicationYear>
  <dates>
    <date dateType="Issued">2017-07-04</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/822737</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.822736</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/zerospeech2017</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;We investigate the extraction of bottle-neck features (BNFs) for multiple languages without access to manual transcription. Multilingual BNFs are derived from a multi-task learning deep neural network which is trained with unsupervised phoneme-like labels. The unsupervised phoneme-like labels are obtained from language-dependent Dirichlet process Gaussian mixture models separately trained on untranscribed speech of multiple languages.&lt;/p&gt;

&lt;blockquote&gt;
&lt;p&gt;In this version, the input MFCC for DPGMM is processed with VTLN.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt; &lt;/p&gt;</description>
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