|Title||An Approach to Natural Speech Understanding Based on Stochastic Models in a Hierarchical Structure|
|Authors||Holger Stahl, Johannes Müller|
|Type||Scientific Conference Paper|
|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
|Reference||In B. Horvat, Z. Kacic (editors): Modern Modes of Man-Machine-Communication (Maribor, Slovenia), pp. 16/1-16/9|
|Download||Scientific Conference Paper as pdf file (60 kByte)|