  Modern end-to-end speech recognition models show astonishing results in
transcribing audio signals into written text. However, conventional data
feeding pipelines may be sub-optimal for low-resource speech recognition, which
still remains a challenging task. We propose an automated curriculum learning
approach to optimize the sequence of training examples based on both the
progress of the model while training and prior knowledge about the difficulty
of the training examples. We introduce a new difficulty measure called
compression ratio that can be used as a scoring function for raw audio in
various noise conditions. The proposed method improves speech recognition Word
Error Rate performance by up to 33% relative over the baseline system
