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2004

Discriminative Language Modeling with Conditional Random Fields and the Perceptron Algorithm

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Discriminative Language Modeling with Conditional Random Fields and the Perceptron Algorithm
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 conditional random fields (CRFs). 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. However, using the feature set output from the perceptron algorithm (initialized with their weights), CRF training provides an additional 0.5% reduction in
Brian Roark, Murat Saraclar, Michael Collins, Mark
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where ACL
Authors Brian Roark, Murat Saraclar, Michael Collins, Mark Johnson
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