Paper: Discriminative Language Modeling With Conditional Random Fields And The Perceptron Algorithm

ACL ID P04-1007
Title Discriminative Language Modeling With Conditional Random Fields And The Perceptron Algorithm
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2004
Authors

This paper describes discriminative language modeling for a large vocabulary speech recognition task. We con- trast two parameter estimation methods: the perceptron algorithm, and a method based on conditional random fields (CRFs). The models are encoded as determin- istic weighted finite state automata, and are applied by intersecting the automata with word-lattices that are the output from a baseline recognizer. The perceptron algo- rithm has the benefit of automatically selecting a rela- tively small feature set in just a couple of passes over the training data.