  The Automated Speech Recognition (ASR) task has been a challenging domain
especially for low data scenarios with few audio examples. This is the main
problem in training ASR systems on the data from low-resource or marginalized
languages. In this paper we present an approach to mitigate the lack of
training data by employing Automated Curriculum Learning in combination with an
adversarial bandit approach inspired by Reinforcement learning. The goal of the
approach is to optimize the training sequence of mini-batches ranked by the
level of difficulty and compare the ASR performance metrics against the random
training sequence and discrete curriculum. We test our approach on a truly
low-resource language and show that the bandit framework has a good improvement
over the baseline transfer-learning model.
