| Title |
Speech Understanding and Speech Translation by Maximum a-posteriori Semantic Decoding |
| Authors |
Johannes Müller, Holger Stahl |
| Abstract |
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. No separate speech recognition stage is needed because of the
integration of the Viterbi-algorithm (calculating acoustic probabilities by the use of
Hidden-Markov-Models) and a probabilistic chart parser (calculating semantic and
syntactic probabilities by special models). The semantic structure is introduced as a
representation of an utterance's meaning. It can be used as an 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 an interlingua-level for a
succeeding language production unit (within an automatic speech translation system for
the creation of spoken output in another language). Following the above principles and
using the respective algorithms, speech understanding and speech translating front-ends
for the domains 'graphic editor', 'service robot', 'medical image visualisation' and
'scheduling dialogues' could be successfully realised. |
| Reference |
Journal "Artificial Intelligence in Engineering",
 Elsevier Publishing, Oxford,
vol. 13 (1999), issue 4, pp. 373-384 |
| Year |
1999 |
| Language |
English |
| Full Paper |
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