Speech Understanding and Speech Translation in Various Domains by Maximum a-posteriori Semantic Decoding

TL;DRAbstract

This paper describes a domain-limited system for speech understanding as well as for speech translation. An integrated semantic decoder directly converts the preprocessed speech signal into its semantic representation by a maximum a-posteriori classification. With the combination of probabilistic knowledge on acoustic, phonetic, syntactic, and semantic levels, the semantic decoder extracts the most probable meaning of the utterance. Any separate speech recognition stage is not needed because of the integration of the Viterbi-algorithm (calculating acoustic probabilities by the use of Hidden-MarkovModels) and a probabilistic chart parser (calculating semantic and syntactic probabilities by especial models). The semantic structure is introduced as representation of an utterance's meaning. It can be used as intermediate level for a succeeding intention decoder (within a speech understanding system for the control of a running application by spoken inputs) as well as interlingua-l

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