An Augmented Chart Data Structure with Efficient Word Lattice Parsing Scheme In Speech Recognition Applications

In this paper, an augmented chart data structure with efficient word lattice parsing scheme in speech recognition applications is proposed. The augmented chart and the associated parsing, algorithm can represent and parse very efficiently a lattice of word hypotheses produced in speech recognition with high degree of lexical ambiguity .without changing the fundamental principles of chart parsing. Every word !attice can be mapped to the augmented chart with the ordering and connection relation among word hypotheses being well preserved in the augmented chart. A jump edge is defined to link edges representing word hypotheses physically separated but practically possible to be connected. Preliminary experimental results show that with the augmented chart parsing all possible constituents o f the input word lattice can be constructed and no constituent needs to be built more than once. This will reduce the computation complexity significantly especially when serious lexical ambiguity exists in the input word lattice as in many speech recognition problems. This augmented chart parsing is thus a very useful and efficient approach to language processing problems in speech recognition applications.