Issues in the development of a stochastic speech understanding system

In the development of a speech understanding system, the recourse to stochastic techniques can greatly reduce the need for human expertise. A known disadvantage is that stochastic models require large annotated training corpora in order to reliably estimate model parameters. Manual semantic annotation of such corpora is tedious, expensive, and subject to inconsistencies. In order to decrease the development cost, this work investigates the performance of stochastic understanding models with two parameters: the use of automatically segmented data and the use of automatically learned lexical normalisation rules.