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Training a robust system, e.g.,Speech to Text (STT), requires large datasets.Variability present in the dataset such as unwanted nuisances and biases arethe reason for the need of large datasets to learn general representations. Inthis work, we propose a novel approach to induce invariance using adversarialforgetting (AF). Our initial experiments on learning invariant features such asaccent on the STT task achieve better generalizations in terms of word errorrate (WER) compared to the traditional models. We observe an absoluteimprovement of 2.2% and 1.3% on out-of-distribution and in-distribution testsets, respectively.

mask-Net: Learning Context Aware Invariant Features using Adversarial Forgetting (Student Abstract)