  End-to-end approaches for automatic speech recognition (ASR) benefit from
directly modeling the probability of the word sequence given the input audio
stream in a single neural network. However, compared to conventional ASR
systems, these models typically require more data to achieve comparable
results. Well-known model adaptation techniques, to account for domain and
style adaptation, are not easily applicable to end-to-end systems. Conventional
HMM-based systems, on the other hand, have been optimized for various
production environments and use cases. In this work, we propose to combine the
benefits of end-to-end approaches with a conventional system using an
attention-based discriminative language model that learns to rescore the output
of a first-pass ASR system. We show that learning to rescore a list of
potential ASR outputs is much simpler than learning to generate the hypothesis.
The proposed model results in 8% improvement in word error rate even when the
amount of training data is a fraction of data used for training the first-pass
system.
