Recognition Performance of a Structured Language Model

A new language model for speech recognition inspired by linguistic analysis is presented. The model develops hidden hierarchical structure incrementally and uses it to extract meaningful information from the word history -- thus enabling the use of extended distance dependencies -- in an attempt to complement the locality of currently used trigram models. The structured language model, its probabilistic parameterization and performance in a two-pass speech recognizer are presented. Experiments on the SWITCHBOARD corpus show an improvement in both perplexity and word error rate over conventional trigram models.

• Type: Journal
• Year: 2000
• Where: CORR
• Authors: Ciprian Chelba, Frederick Jelinek
• Keywords: Conventional Trigram Models, CORR 2000, Education, Language Model, Trigram Models