Robust analysis of spoken input combining statistical and knowledge-based information sources

The paper is concerned with the analysis of automatic transcription of spoken input into an interlingua formalism for a speech-to-speech machine translation system. This process is based on two sub-tasks: (1) the recognition of the domain action (a speech act and a sequence of concepts); (2) the extraction of arguments consisting of feature-value information. Statistical models are used for the former, while a knowledge-based approach is employed for the latter. The paper proposes an algorithm that improves the analysis in terms of robustness and performance; it combines the scores of the statistical models with the extracted arguments, taking into account the well-formedness constraints defined by the interlingua formalism.