The analysis and interpretation of time constituents is important for most applications of speech understanding systems. Problems can be caused by the varying distribution of constituents. A basic set of time constituents were found in a corpus of domain specific (train schedule) utterances. A distributed representation of surface structure models and an incremental semantic analysis is used to manage the complexity. The knowledge base of the speecli understanding system that provides the framework for the analysis and interpretation of time constituents uses the semantic network language ERNEST.
Keywords: Speech Understanding, Time Constituents