The CMU air travel information service: understanding spontaneous speech
HLT '90 Proceedings of the workshop on Speech and Natural Language
Recent improvements in the CMU spoken language understanding system
HLT '94 Proceedings of the workshop on Human Language Technology
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
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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 ($\widehat{=} 100\%$) 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 1 300 sentences.