Neural Lattice Search for Speech Recognition

To improve the accuracy of automatic speech recognition, a two-pass decoding strategy is widely adopted. The first-pass model generates compact word lattices, which are utilized by the second-pass model to perform rescoring. Currently, the most popular rescoring methods are N-best rescoring and lattice rescoring with long short-term memory language models (LSTMLMs). However, these methods encounter the problem of limited search space or inconsistency between training and evaluation. In this paper, we address these problems with an end-to-end model for accurately extracting the best hypothesis from the word lattice. Our model is composed of a bidirectional LatticeLSTM encoder followed by an attentional LSTM decoder. The model takes word lattice as input and generates the single best hypothesis from the given lattice space. When combined with an LSTMLM, the proposed model yields 9.7% and 7.5% relative WER reduction compared to N-best rescoring methods and lattice rescoring methods within the same amount of decoding time.