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arXiv · 2112.02498

Consistent Training and Decoding For End-to-end Speech Recognition Using Lattice-free MMI

Jinchuan Tian·Jianwei Yu·Chao Weng·Shi-Xiong Zhang·Dan Su·Dong Yu·Yuexian Zou
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Abstract

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.

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Jinchuan Tian, Jianwei Yu, Chao Weng, Shi-Xiong Zhang, Dan Su, Dong Yu, Yuexian Zou. 2021-12-05. Consistent Training and Decoding For End-to-end Speech Recognition Using Lattice-free MMI. https://arxiv.org/abs/2112.02498

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