Consistent Training and Decoding For End-to-end Speech Recognition Using\n Lattice-free MMI
Journal: arXiv (Cornell University)
Year: 2021

Recently, End-to-End (E2E) frameworks have achieved remarkable results on\nvarious Automatic Speech Recognition (ASR) tasks. However, Lattice-Free Maximum\nMutual Information (LF-MMI), as one of the discriminative training criteria\nthat show superior performance in hybrid ASR systems, is rarely adopted in E2E\nASR frameworks. In this work, we propose a novel approach to integrate LF-MMI\ncriterion into E2E ASR frameworks in both training and decoding stages. The\nproposed approach shows its eff