Please use this identifier to cite or link to this item: http://repository.iiitd.edu.in/xmlui/handle/123456789/730
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dc.contributor.authorVenkadeswaran, Aravaida Kumaran-
dc.contributor.authorAnand, Saket (Advisor)-
dc.date.accessioned2019-10-04T10:58:41Z-
dc.date.available2019-10-04T10:58:41Z-
dc.date.issued2019-04-28-
dc.identifier.urihttp://repository.iiitd.edu.in/xmlui/handle/123456789/730-
dc.description.abstractAutomatic Speech Recognition which is aimed at enabling a more natural form of human-machine interaction has been an area of research for decades now and many breakthroughs have been made in this eld. The performance of many state-of-the-art systems for mainstream languages and accents are extremely good. And there is a need for zero-resource or minimal resource systems as gathering enough data is highly challenging and sometimes near impossible. Through this study, we aim to provide a proof of concept for the idea of using speech embeddings for Automatic Speech Recognition.en_US
dc.language.isoen_USen_US
dc.publisherIIITD-Delhien_US
dc.subjectAutomatic Speech Recognitionen_US
dc.subjectSemi Supervisionen_US
dc.subjectDomain Adaptationen_US
dc.subjectSpeechen_US
dc.subjectAc- cent Invarianceen_US
dc.subjectWord embeddingsen_US
dc.subjectUnsuperviseden_US
dc.subjectSpeech2Vecen_US
dc.subjectRaw Speechen_US
dc.titleSemi supervised accent invariant speech recognitionen_US
dc.typeOtheren_US
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