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ICASSP
2008
IEEE

Discriminative feature weighting using MCE training for topic identification of spoken audio recordings

14 years 17 days ago
Discriminative feature weighting using MCE training for topic identification of spoken audio recordings
In this paper we investigate a discriminative approach to feature weighting for topic identification using minimum classification error (MCE) training. Our approach learns feature weights by optimizing an objective loss function directly related to the classification error rate of the topic identification system. Topic identification experiments are performed on spoken conversations from the Fisher corpus. Features drawn from both word and phone lattices generated via automatic speech recognition are investigated. Under various different conditions, our new feature weighting scheme reduces our classification error rate between 9% and 23% relative to our baseline naive Bayes system using feature selection.
Timothy J. Hazen, Anna Margolis
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICASSP
Authors Timothy J. Hazen, Anna Margolis
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