Please use this identifier to cite or link to this item: doi:10.22028/D291-25330
Title: Improving parsing of spontaneous speech with the help of prosodic boundaries
Author(s): Kompe, Ralf
Kießling, Andreas
Niemann, Heinrich
Nöth, Elmar
Batliner, Anton
Schacht, S.
Ruland, T.
Block, H. U.
Language: English
Year of Publication: 1997
SWD key words: Künstliche Intelligenz
Free key words: artificial intelligence
DDC notations: 004 Computer science, internet
Publikation type: Report
Abstract: Parsing can be improved in automatic speech understanding if prosodic boundary marking is taken into account, because syntactic boundaries are often marked by prosodic means. Because large databases are needed for the training of statistical models for prosodic boundaries, we developed a labeling scheme for syntactic-prosodic boundaries within the German VERBMOBIL project (automatic speech-to-speech translation). We compare the results of classifiers (multi-layer perceptrons and language models) trained on these syntactic-prosodic boundary labels with classifiers trained on perceptual-prosodic and purely syntactic labels. Recognition rates of up to 96% were achieved. The turns that we need to parse consist of 20 words on the average and frequently contain sequences of partial sentence equivalents due to restarts, ellipsis, etc. For this material, the boundary scores computed by our classifiers can successfully be integrated into the syntactic parsing of word graphs; currently, they improve the parse time by 92% and reduce the number of parse trees by 96%. This is achieved by introducing a special Prosodic Syntactic Clause Boundary symbol (PSCB) into our grammar and guiding the search for the best word chain with the prosodic boundary scores.
Link to this record: urn:nbn:de:bsz:291-scidok-54848
Series name: Vm-Report / Verbmobil, Verbundvorhaben, [Deutsches Forschungszentrum für Künstliche Intelligenz]
Series volume: 210
Date of registration: 10-Sep-2013
Faculty: SE - Sonstige Einrichtungen
Department: SE - DFKI Deutsches Forschungszentrum für Künstliche Intelligenz
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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