  We propose a direct-to-word sequence model which uses a word network to learn
word embeddings from letters. The word network can be integrated seamlessly
with arbitrary sequence models including Connectionist Temporal Classification
and encoder-decoder models with attention. We show our direct-to-word model can
achieve word error rate gains over sub-word level models for speech
recognition. We also show that our direct-to-word approach retains the ability
to predict words not seen at training time without any retraining. Finally, we
demonstrate that a word-level model can use a larger stride than a sub-word
level model while maintaining accuracy. This makes the model more efficient
both for training and inference.
