Mismatched Crowdsourcing from Multiple Annotator Languages for Recognizing Zero-Resourced Languages: A Nullspace Clustering Approach

Abstract

It is extremely challenging to create training labels for building acoustic models of zero-resourced languages, in which conventional resources required for model training – lexicons, transcribed audio, or in extreme cases even orthographic system or a viable phone set design for the language – are unavailable. Here, language mismatched transcripts, in… (More)

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