Multilevel semantic analysis in an automatic speech understanding and dialog system

Abstract

At our institute a speech understanding and dialog system is developed. As an example we model an information system for timetables and other information about intercity trains. In understanding spoken utterances, additional problems arise due to pronunciation variabilities and vagueness of the word recognition process. Experiments so far have also shown that the syntactical analysis produces a lot more hypotheses instead of reducing the number of word hypotheses. The reason for that is the possibility o! combining nearly every group of word hypotheses which are adjacent with respect to the speech signal to a syntactically correct constituent. Also, the domain independent semantic analysis cannot be used for filtering, because a syntactic sentence hypothesis normally can be interp.reted in several different ways, respectively a set of syntactic hypotheses for constituents can be combined to a lot of semantically interpretible sentences. Because of this combinatorial explcaiun it seems to be reasonable to introduce domain dependent and contextual knowledge as early as possible, also for the semantic analysis. On the other hand it would be more efficient prior to the whole semantic interpretation of each syntactic hypothesis or combination of syntactic hypotheses to find possible candidates with less effort and interpret only the more probable Ones.