Automatic Extraction of Language Models from a Linguistic Knowledge Base
Gernot A. Fink, Gerhard Sagerer, Franz Kümmert · 1992
We present an algorithm for the extraction of language models from a semantic network that contains syntactic, semantic and pragmatic knowledge. The use of such language models in acoustic recognition processes results in much better system performance in speed as well as in quality of results. The automatic extraction process guarantees that the created models are always up to date and consistent with the knowledge base. The algorithm can be applied to simple constituents as well as to concepts representing an entire task domain. Keywords: Speech Processing, Speech Recognition. * This research was supported by the German Ministery of Research and Technology (BMFT) under grant number 01IV102A0. Only the authors are responsible for the contents of this publication. 1 Motivation Most approaches to word recognition that are currently used are based upon Hidden-MarkovModels (HMMs). A HMM stochastically describes a class of segments within a speech signal. Small models can be combined to...