A first-pass decoding strategy for LSTM language models in LVCSR recombines hypotheses on the last two words before lattice rescoring to achieve competitive results on Hub5'00 and Librispeech with better-than-real-time runtime.
Real-time one-pass d e- coding with recurrent neural network language model for spe ech recognition,
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LSTM Language Models for LVCSR in First-Pass Decoding and Lattice-Rescoring
A first-pass decoding strategy for LSTM language models in LVCSR recombines hypotheses on the last two words before lattice rescoring to achieve competitive results on Hub5'00 and Librispeech with better-than-real-time runtime.