Lattice Rescoring Strategies for Long Short Term Memory Language Models in Speech Recognition
Table 3: WER of LSTMLM lattice rescoring algorithms
['Algorithm', 'WER (%)', '[EMPTY]']
While the WER reduced by 2% relative for the 2-gram lattices, it showed no variation for the 5-gram lattices. The push-forward algorithm with k=1 results in a more severe approximation for the 2-gram lattices. Hence, increasing the LSTM hypotheses per lattice node (k) results in a better WER. We next expanded the lattice to different N-gram orders, up to a maximum of 6, prior to applying the push-forward algorithm with k=1. While 2-gram lattices benefitted considerably from lattice expansion (3% relative), there was less gain for 5-gram lattices which already contained unique 5-gram histories for most lattice nodes prior to expansion. LSTM state pooling