This paper introduces an novel framework for speech understanding using extended context-free grammars (ECFGs) by combining statistical methods and rule based knowledge. By only using 1st level labels a considerable lower expense of annotation effort can be achieved. In this paper we derive hierarchical non-deterministic automata from the ECFGs, which are transformed into transition networks (TNs) representing all kinds of labels. A sequence of recognized words is hierarchically decoded by using a Viterbi algorithm. In experiments the difference between a hand-labeled tree bank annotation and our approach is evaluated. The conducted experiments show the superiority of our proposed framework. Comparing to a hand-labeled baseline system (=100%) we achieve 95,4% acceptance rate for complete sentences and 97.8% for words. This induces an accuray rate of 95.1% and error rate of 4.9%, respectively F1-measure 95.6% in a corpus of 1300 sentences.
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