Discriminative syntactic language modeling for speech recognition

  • Authors:
  • Michael Collins;Brian Roark;Murat Saraclar

  • Affiliations:
  • MIT CSAIL;OGI/OHSU;Bogazici University

  • Venue:
  • ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
  • Year:
  • 2005

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Abstract

We describe a method for discriminative training of a language model that makes use of syntactic features. We follow a reranking approach, where a baseline recogniser is used to produce 1000-best output for each acoustic input, and a second "reranking" model is then used to choose an utterance from these 1000-best lists. The reranking model makes use of syntactic features together with a parameter estimation method that is based on the perception algorithm. We describe experiments on the Switchboard speech recognition task. The syntactic features provide an additional 0.3% reduction in test-set error rate beyond the model of (Roark et al., 2004a; Roark et al., 2004b) (significant at p