Semi-Supervised Learning with Data Augmentation for End-to-End ASR

27 Jul 2020Felix WeningerFranco ManaRoberto GemelloJesús Andrés-FerrerPuming Zhan

In this paper, we apply Semi-Supervised Learning (SSL) along with Data Augmentation (DA) for improving the accuracy of End-to-End ASR. We focus on the consistency regularization principle, which has been successfully applied to image classification tasks, and present sequence-to-sequence (seq2seq) versions of the FixMatch and Noisy Student algorithms... (read more)

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