| Abstract |
In this paper, an approach for understanding natural speech by means of two stochastic
knowledge bases is presented: Within a given domain, the semantic model generates possible
semantic structures, which are semantic representations close to the word level.
Corresponding to such a semantic structure, the syntactic model generates word chains
using hierarchical Hidden-Markov-Models. Integrated into a speech understanding system,
these stochastic knowledge bases can be utilized for a 'top-down' approach.
Keywords: speech recognition, language understanding, Hidden-Markov-Model, spoken
human-machine-dialogue |
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