Bootstrap and aggregating VQ classifier for speaker recognition

Abstract

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.