  RNN-T is currently considered the industry standard in ASR due to its
exceptional WERs in various benchmark tests and its ability to support seamless
streaming and longform transcription. However, its biggest drawback lies in the
significant discrepancy between its training and inference objectives. During
training, RNN-T maximizes all alignment probabilities by teacher forcing, while
during inference, it uses beam search which may not necessarily find the
maximum probable alignment. Additionally, RNN-T's inability to experience
mistakes during teacher forcing training makes it more problematic when a
mistake occurs in inference. To address this issue, this paper proposes a
Reinforcement Learning method that minimizes the gap between training and
inference time. Our Edit Distance based RL (EDRL) approach computes rewards
based on the edit distance, and trains the network at every action level. The
proposed approach yielded SoTA WERs on LibriSpeech for the 600M Conformer RNN-T
model.
