  Regularization is important for end-to-end speech models, since the models
are highly flexible and easy to overfit. Data augmentation and dropout has been
important for improving end-to-end models in other domains. However, they are
relatively under explored for end-to-end speech models. Therefore, we
investigate the effectiveness of both methods for end-to-end trainable, deep
speech recognition models. We augment audio data through random perturbations
of tempo, pitch, volume, temporal alignment, and adding random noise.We further
investigate the effect of dropout when applied to the inputs of all layers of
the network. We show that the combination of data augmentation and dropout give
a relative performance improvement on both Wall Street Journal (WSJ) and
LibriSpeech dataset of over 20%. Our model performance is also competitive with
other end-to-end speech models on both datasets.
