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CSL
2007
Springer

Discriminative n-gram language modeling

13 years 3 months ago
Discriminative n-gram language modeling
This paper describes discriminative language modeling for a large vocabulary speech recognition task. We contrast two parameter estimation methods: the perceptron algorithm, and a method based on maximizing the regularized conditional log-likelihood. The models are encoded as deterministic weighted finite state automata, and are applied by intersecting the automata with word-lattices that are the output from a baseline recognizer. The perceptron algorithm has the benefit of automatically selecting a relatively small feature set in just a couple of passes over the training data. We describe a method based on regularized likelihood that makes use of the feature set given by the perceptron algorithm, and initialization with the perceptron’s weights; this method gives an addition 0.5% reduction in word error
Brian Roark, Murat Saraclar, Michael Collins
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2007
Where CSL
Authors Brian Roark, Murat Saraclar, Michael Collins
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