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arXiv · 1810.11352

A novel pyramidal-FSMN architecture with lattice-free MMI for speech recognition

Xuerui Yang·Jiwei Li·Xi Zhou
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Abstract

Deep Feedforward Sequential Memory Network (DFSMN) has shown superior performance on speech recognition tasks. Based on this work, we propose a novel network architecture which introduces pyramidal memory structure to represent various context information in different layers. Additionally, res-CNN layers are added in the front to extract more sophisticated features as well. Together with lattice-free maximum mutual information (LF-MMI) and cross entropy (CE) joint training criteria, experimental results show that this approach achieves word error rates (WERs) of 3.62% and 10.89% respectively on Librispeech and LDC97S62 (Switchboard 300 hours) corpora. Furthermore, Recurrent neural network language model (RNNLM) rescoring is applied and a WER of 2.97% is obtained on Librispeech.

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Xuerui Yang, Jiwei Li, Xi Zhou. 2018-10-26. A novel pyramidal-FSMN architecture with lattice-free MMI for speech recognition. https://arxiv.org/abs/1810.11352

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