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As an example, an algorithm might be fed a smaller quantity of labeled speech data after which you can experienced on a much larger set of unlabeled speech data as a way to develop a machine learning model able to speech recognition.Semi-supervised learning can clear up the trouble of not acquiring more than enough labeled data for a supervised lea