Generative models have long been the dominant approach for speech recognition. The
success of these models however relies on the use of sophisticated recipes and complicated
machinery that is not easily accessible to non-practitioners. Recent innovations in Deep
Learning have given rise to an alternative-discriminative models called Sequence-to-
Sequence models, that can almost match the accuracy of state of the art generative models.
While these models are easy to train as they can be trained end-to-end in a single step, they …