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Towards Representative Subset Selection for Self-Supervised Speech Recognition. (arXiv:2203.09829v2 [cs.LG] UPDATED)
June 27, 2022, 1:11 a.m. | Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha Ali Raza
cs.LG updates on arXiv.org arxiv.org
Self-supervised speech recognition models require considerable labeled
training data for learning high-fidelity representations for Automatic Speech
Recognition (ASR) which is computationally demanding and time-consuming,
thereby hindering the usage of these models in resource-constrained
environments. We consider the task of identifying an optimal subset of data to
train self-supervised speech models for ASR. We make a surprising observation
that the dataset pruning strategies used in vision tasks for sampling the most
informative examples do not perform better than random subset selection …
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