An Investigation of Few-Shot Learning in Spoken Term Classification

Keyword spotting with limited training data is a challenging task which can be treated as a few-shot learning problem. In this paper, we present a meta-learning approach which learns a good initialization of the base KWS model from existed labeled dataset. Then it can quickly adapt to new tasks of keyword spotting with only a few labeled data. Furthermore, to strengthen the ability of distinguishing the keywords with the others, we incorporate the negative class as external knowledge to the meta-training process, which proves to be effective. Experiments on the Google Speech Commands dataset show that our proposed approach outperforms the baselines.