Paper: Discriminative Syntactic Language Modeling For Speech Recognition

ACL ID P05-1063
Title Discriminative Syntactic Language Modeling For Speech Recognition
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005
Authors

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 sec- ond “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 parame- ter estimation method that is based on the perceptron algorithm. We describe exper- iments on the Switchboard speech recog- nition 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) (signifi- cant at p < 0.001), which makes use of a discriminatively trained n-gram model, giving a total red...