Large-scale Discriminative Language Model Reranking for Voice Search
Venue
Proceedings of the NAACL-HLT 2012 Workshop: Will We Ever Really Replace the N-gram Model? On the Future of Language Modeling for HLT, Association for Computational Linguistics, pp. 41-49
Publication Year
2012
Authors
Preethi Jyothi, Leif Johnson, Ciprian Chelba, Brian Strope
BibTeX
Abstract
We present a distributed framework for large-scale discriminative language models
that can be integrated within a large vocabulary continuous speech recognition
(LVCSR) system using lattice rescoring. We intentionally use a weakened acoustic
model in a baseline LVCSR system to generate candidate hypotheses for voice-search
data; this allows us to utilize large amounts of unsupervised data to train our
models. We propose an efficient and scalable MapReduce framework that uses a
perceptron-style distributed training strategy to handle these large amounts of
data. We report small but significant improvements in recognition accuracies on a
standard voice-search data set using our discriminative reranking model. We also
provide an analysis of the various parameters of our models including model size,
types of features, size of partitions in the MapReduce framework with the help of
supporting experiments.
