Abstract

Current speech understanding systems are typically designed as multi-stage systems, although this theoretically gives rise to errors due to early decisions. We present a framework that offers the chance of reducing these errors by an integrated system which directly computes a semantic tree representation from the input speech signal through a token passing based one-stage decoder, called ODINS. In order to limit the complexity of ODINS, we represent all a-priori knowledge consistently by a generalized uniform knowledge model based on a hierarchy of probabilistic transition networks, which also can be n-grams. Our framework includes a method to evaluate the system output using an edit distance based tree matching algorithm. First experiments quantify and confirm the theoretical advantage of the one-stage strategy over a corresponding two-stage approach.

Citations