Assessment of dialogue systems by means of a new simulation technique
Speech Communication
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Context-based speech recognition error detection and correction
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
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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.