This paper proposes a new technique to implicitly correct some ASR
errors made by spoken dialogue systems, which is implemented at two levels:
statistical and linguistic. The goal of the former level is to employ for the correction
knowledge extracted from the analysis of a training corpus comprised of
utterances and their corresponding ASR results. The outcome of the analysis is
a set of syntactic-semantic models and a set of lexical models, which are optimally
selected during the correction. The goal of the correction at the linguistic
level is to repair errors not detected during the statistical level which affects the
semantics of the sentences. Experiments carried out with a previouslydeveloped
spoken dialogue system for the fast food domain indicate that the
technique allows enhancing word accuracy, spoken language understanding and
task completion by 8.5%, 16.54% and 44.17% absolute, respectively.Ministerio de Ciencia y Tecnología TIN2007-64718 HAD
