Purely sequence-trained neural networks for ASR based on lattice-free MMI

no code implementations • INTERSPEECH 2016 2016 • Daniel Povey, Vijayaditya Peddinti, Daniel Galvez, Pegah Ghahrmani, Vimal Manohar, Xingyu Na, Yiming Wang, Sanjeev Khudanpur

Models trained with LFMMI provide a relative word error rate reduction of ∼11. 5%, over those trained with cross-entropy objective function, and ∼8%, over those trained with cross-entropy and sMBR objective functions.

Language Modelling Speech Recognition