Spoken document classification with SVMs using linguistic unit weighting and probabilistic couplers

Uri Lurgel, Gerhard Rigoll

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The task addressed by this paper is spoken document classification (SDC) of German TV news with Support Vector Machines (SVMs). It shows the benefits of weighting different linguistic units when combined into one feature vector. Further experiments show that probabilistic SVMs (pSVMs) with recently introduced couplers perform well on a SDC task. New couplers for multi-category classification, both for pSVMs and non-pSVMs, will be discussed. They are easy to implement and show good and promising results. It turns out that using the distance Instead of the decision value can be favorable. Theoretical justification is given for our approaches, and some results are explained theoretically.

Original languageEnglish
Title of host publicationProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
EditorsJ. Kittler, M. Petrou, M. Nixon
Pages667-670
Number of pages4
DOIs
StatePublished - 2004
EventProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 - Cambridge, United Kingdom
Duration: 23 Aug 200426 Aug 2004

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2
ISSN (Print)1051-4651

Conference

ConferenceProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
Country/TerritoryUnited Kingdom
CityCambridge
Period23/08/0426/08/04

Fingerprint

Dive into the research topics of 'Spoken document classification with SVMs using linguistic unit weighting and probabilistic couplers'. Together they form a unique fingerprint.

Cite this