Bootstrap and aggregating VQ classifier for speaker recognition

Bootstrap and aggregating VQ classifier for speaker recognition A bootstrap and aggregating (bagging) vector quantisation (VQ) classifier is proposed for speaker recognition. This method obtains multiple training data sets by resampling the original training data set and then integrates the corresponding multiple classifiers into a single classifier. Experiments involving a closed set, text-independent and speaker identification system are carried out using the TIMIT database. The proposed bagging VQ classifier shows considerably improved performance over the conventional VQ classifier.
Publisher
IEE-INST ELEC ENG
Issue Date
1999-06
Language
ENG
Citation

ELECTRONICS LETTERS, v.35, no.12, pp.973 - 974

ISSN
0013-5194
URI
http://hdl.handle.net/10203/73523
Appears in Collection
EE-Journal Papers(저널논문)
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