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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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        <foaf:name>Haizhou Li</foaf:name>
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    <dct:title>Pairwise Learning using Unsupervised Bottleneck Features for Zero-Resource Speech Challenge 2017 (System 1)</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2017</dct:issued>
    <dcat:keyword>pairwise learning</dcat:keyword>
    <dcat:keyword>zero-resource</dcat:keyword>
    <dcat:keyword>unsupervised bottleneck features</dcat:keyword>
    <dcat:keyword>neural networks</dcat:keyword>
    <dcat:keyword>autoencoder</dcat:keyword>
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    <dct:description>&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;</dct:description>
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