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 perceptron 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 < 0.001), which makes use of a discriminatively trained n-gram model, giving a total reduction of 1.2% over the baseline Switchboard system.
REF present a corrective language model based on a discriminative framework.
Discriminative Syntactic Language Modeling For Speech Recognition
Annual Meeting Of The Association For Computational Linguistics
Computer Science