  In this paper, we investigate the feasibility of applying few-shot learning
algorithms to a speech task. We formulate a user-defined scenario of spoken
term classification as a few-shot learning problem. In most few-shot learning
studies, it is assumed that all the N classes are new in a N-way problem. We
suggest that this assumption can be relaxed and define a N+M-way problem where
N and M are the number of new classes and fixed classes respectively. We
propose a modification to the Model-Agnostic Meta-Learning (MAML) algorithm to
solve the problem. Experiments on the Google Speech Commands dataset show that
our approach outperforms the conventional supervised learning approach and the
original MAML.
