Please use this identifier to cite or link to this item: doi:10.22028/D291-25327
Title: Topic spotting using subword units
Author(s): Warnke, V.
Harbeck, Stefan
Niemann, Heinrich
Nöth, Elmar
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: In this paper we present a new approach for topic spotting based on subword units and feature vectors instead of words. In our first approach, we only use vector quantized feature vectors and polygram language models for topic representation. In the second approach, we use phonemes instead of the vector quantized feature vectors and model the topics again using polygram language models. We trained and tested the two methods on two different corpora. The first is a part of a media corpus which contains data from TV shows for three different topics. The second is the VERBMOBIL-corpus where we used 18 dialog acts as topics. Each corpus was splitted into disjunctive test and training sets. We achieved recognition rates up to 82% for the three topics of the media corpus and up to 64% using 18 dialog acts of the VERBMOBIL-corpus as topics.
Link to this record: urn:nbn:de:bsz:291-scidok-54736
Series name: Vm-Report / Verbmobil, Verbundvorhaben, [Deutsches Forschungszentrum für Künstliche Intelligenz]
Series volume: 205
Date of registration: 27-Aug-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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