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

Understand

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.