Discriminative Language Modeling with Conditional Random Fields and the Perceptron Algorithm

@inproceedings{Roark2004DiscriminativeLM,
  title={Discriminative Language Modeling with Conditional Random Fields and the Perceptron Algorithm},
  author={Brian Roark and Murat Saraclar and Michael Collins and Mark Johnson},
  booktitle={ACL},
  year={2004}
}
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… CONTINUE READING
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  • However, using the feature set output from the perceptron algorithm (initialized with their weights), CRF training provides an additional 0.5% reduction in word error rate, for a total 1.8% absolute reduction from the baseline of 39.2%.

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