| Abstract |
A new form of a grammar is described, which provides two separate sets of stochastic
parameters for representing both the semantic and the syntactic knowledge, required for
automatic speech understanding. The semantic structure is introduced as an adequate
representation of natural spoken, one-sentence command utterances. The constraints and
probabilities delivered by the grammar can be integrated into the framework of a stochastic
topdown parser to decode the semantic content of an utterance directly from its observation
sequence. The performance of the developed methods is proved for the domain of a speech
understanding graphic editor, which can be controlled solely by natural spoken commands.
Keywords: speech understanding, context-free grammar, stochastic models, syntactic
and semantic knowledge |
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