  Children's automatic speech recognition (ASR) is always difficult due to, in
part, the data scarcity problem, especially for kindergarten-aged kids. When
data are scarce, the model might overfit to the training data, and hence good
starting points for training are essential. Recently, meta-learning was
proposed to learn model initialization (MI) for ASR tasks of different
languages. This method leads to good performance when the model is adapted to
an unseen language. However, MI is vulnerable to overfitting on training tasks
(learner overfitting). It is also unknown whether MI generalizes to other
low-resource tasks. In this paper, we validate the effectiveness of MI in
children's ASR and attempt to alleviate the problem of learner overfitting. To
achieve model-agnostic meta-learning (MAML), we regard children's speech at
each age as a different task. In terms of learner overfitting, we propose a
task-level augmentation method by simulating new ages using frequency warping
techniques. Detailed experiments are conducted to show the impact of task
augmentation on each age for kindergarten-aged speech. As a result, our
approach achieves a relative word error rate (WER) improvement of 51% over the
baseline system with no augmentation or initialization.
