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Distributed discriminative language models for Google voice-search

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4 Author(s)
Jyothi, P. ; Ohio State Univ., Columbus, OH, USA ; Johnson, L. ; Chelba, C. ; Strope, B.

This paper considers large-scale linear discriminative language models trained using a distributed perceptron algorithm. The algorithm is implemented efficiently using a MapReduce/SSTable framework. This work also introduces the use of large amounts of unsupervised data (confidence filtered Google voice-search logs) in conjunction with a novel training procedure that regenerates word lattices for the given data with a weaker acoustic model than the one used to generate the unsupervised transcriptions for the logged data. We observe small but statistically significant improvements in recognition performance after reranking N-best lists of a standard Google voice-search data set.

Published in:

Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on

Date of Conference:

25-30 March 2012