This paper describes the key features of SOUP, a stochastic, chart-based, top-down parser, especially engineered for real-time analysis of spoken language with very large, multi-domain semantic grammars. SOUP achieves flexibility by encoding context-free grammars, specified for example in the Java Speech Grammar Format, as probabilistic recursive transition networks, and robustness by allowing skipping of input words at any position and producing ranked interpretations that may consist of multiple parse trees. Moreover, SOUP is very efficient, which allows for practically instantaneous backend response.

The MT engine of the JANUS speech-to-speech translation system is designed around four main principles: 1) an interlingua approach that allows the efficient addition of new languages, 2) the use of semantic grammars that yield low cost high quality translations for limited domains, 3) modular grammars that support easy expansion into new domains, and 4) efficient integration of multiple grammars using multi-domain parse lattices and domain re-scoring. Within the framework of the C-STAR-II speech-to-speech translation effort, these principles are tested against the challenge of providing translation for a number of domains and language pairs with the additional restriction of a common interchange format.