  Recently, End-to-End (E2E) frameworks have achieved remarkable results on
various Automatic Speech Recognition (ASR) tasks. However, Lattice-Free Maximum
Mutual Information (LF-MMI), as one of the discriminative training criteria
that show superior performance in hybrid ASR systems, is rarely adopted in E2E
ASR frameworks. In this work, we propose a novel approach to integrate LF-MMI
criterion into E2E ASR frameworks in both training and decoding stages. The
proposed approach shows its effectiveness on two of the most widely used E2E
frameworks including Attention-Based Encoder-Decoders (AEDs) and Neural
Transducers (NTs). Experiments suggest that the introduction of the LF-MMI
criterion consistently leads to significant performance improvements on various
datasets and different E2E ASR frameworks. The best of our models achieves
competitive CER of 4.1\% / 4.4\% on Aishell-1 dev/test set; we also achieve
significant error reduction on Aishell-2 and Librispeech datasets over strong
baselines.
