Empowering Low-Resource Language ASR via Large-Scale Pseudo Labeling
Empowering Low-Resource Language ASR via Large-Scale Pseudo Labeling
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Ithaca: Cornell University Library, arXiv.org
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English
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Publisher
Ithaca: Cornell University Library, arXiv.org
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Contents
In this study, we tackle the challenge of limited labeled data for low-resource languages in ASR, focusing on Hindi. Specifically, we explore pseudo-labeling, by proposing a generic framework combining multiple ideas from existing works. Our framework integrates multiple base models for transcription and evaluators for assessing audio-transcript pa...
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Empowering Low-Resource Language ASR via Large-Scale Pseudo Labeling
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TN_cdi_proquest_journals_3097601876
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_3097601876
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E-ISSN
2331-8422
