Modeling Of Time Constituents For Speech Understanding

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 speech 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 1. INTRODUCTION In a speech understanding system there are several domains of analysis and interpretation. Firstly, the system has to recognize single words in a torrent of speech sounds. Secondly, the system combines words to constituents, i.e. it performs a syntactic analysis. It also has to reconstruct the meaning of the utterance in questio..