Mask-Net: Learning Context Aware Invariant Features Using Adversarial Forgetting (Student Abstract)

Training a robust system, e.g., Speech to Text (STT), requires large datasets. Variability present in the dataset, such as unwanted nuances and biases, is the reason for the need for large datasets to learn general representations. In this work, we propose a novel approach to induce invariance using adversarial forgetting (AF). Our initial experiments on learning invariant features such as accent on the STT task achieve better generalizations in terms of word error rate (WER) compared to traditional models. We observe an absolute improvement of 2.2% and 1.3% on out-of-distribution and in-distribution test sets, respectively.

Authors

• Hemant Yadav IIIT Delhi, India
• Rajiv Ratn Shah Indraprastha Institute of Information Technology, Delhi

Keywords

• Adversarial Forgetting
• ASR
• Invariant Features
• Robust Speech To Text System