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Efficient lattice rescoring using recurrent neural network language models
This is the accepted manuscript of a paper published in the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, Issue Date: 4-9 May 2014, Written by: Liu, X.; Wang, Y.; Chen, X.; Gales, M.J.F.; Woodland, P.C.).Recurrent neural network language models (RNNLM) have become
an increasingly popular choice for state-of-the-art speech recognition
systems due to their inherently strong generalization performance.
As these models use a vector representation of complete
history contexts, RNNLMs are normally used to rescore N-best lists.
Motivated by their intrinsic characteristics, two novel lattice rescoring
methods for RNNLMs are investigated in this paper. The first
uses an n-gram style clustering of history contexts. The second approach
directly exploits the distance measure between hidden history
vectors. Both methods produced 1-best performance comparable
with a 10k-best rescoring baseline RNNLMsystem on a large vocabulary
conversational telephone speech recognition task. Significant
lattice size compression of over 70% and consistent improvements
after confusion network (CN) decoding were also obtained over the
N-best rescoring approach.The research leading to these results was supported by EPSRC grant
EP/I031022/1 (Natural Speech Technology) and DARPA under the Broad
Operational Language Translation (BOLT) and RATS programs
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