This paper presents an attempt at using the syntactic structure in natural language for improved language models for speech recognition. The structured language model merges techniques in automatic parsing and language modeling using an original probabilistic parameterization of a shift-reduce parser. A maximum likelihood re-estimation procedure belonging to the class of expectation-maximization algorithms is employed for training the model. Experiments on the Wall Street Journal and Switchboard corpora show improvement in both perplexity and word error rate-word lattice rescoring-over the standard 3-gram language model.
An SLM for English REF , proposed as a language model for speech recognition, is also based on a CFG.
Structured language modeling
Comput. Speech Lang.
Comput. Speech Lang.
Computer Science