Mitigating Catastrophic Forgetting for Few-Shot Spoken Word
  Classification Through Meta-Learning
Ruan van der Merwe Herman Kamper
We consider the problem of few-shot spoken word classification in a setting
where a model is incrementally introduced to new word classes. This would occur
in a user-defined keyword system where new words can be added as the system is
used. In such a continual learning scenario, a model might start to misclassify
earlier words as newer classes are added, i.e. catastrophic forgetting. To
address this, we propose an extension to model-agnostic meta-learning (MAML):
each inner learning loop, where a model "learns how to learn'' new classes,
ends with a single gradient update using stored templates from all the classes
that the model has already seen (one template per class). We compare this
method to OML (another extension of MAML) in few-shot isolated-word
classification experiments on Google Commands and FACC. Our method consistently
outperforms OML in experiments where the number of shots and the final number
of classes are varied.