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.

本文研究了在用户定义的关键词系统中增量引入新词类的少样本语音单词分类问题，并提出了通过模型无关元学习 (MAML) 的扩展来解决灾难性遗忘的问题。在 Google Commands 和 FACC 上进行的实验表明，我们的方法在快速学习少量词汇的分类问题上优于 MAML 的另一种扩展 OML。

元学习减轻少样本口语词分类的灾难性遗忘问题