Speaker verification and spoken language identification using a generalized i-vector framework with phonetic tokenizations and tandem features
Ming Li, Wenbo Liu · 2014
This paper presents a generalized i-vector framework with pho-netic tokenizations and tandem features for speaker verification as well as language identification. First, the tokens for cal-culating the zero-order statistics is extended from the MFCC trained Gaussian Mixture Models (GMM) components to pho-netic phonemes, 3-grams and tandem feature trained GMM components using phoneme posterior probabilities. Second, given the calculated zero-order statistics (posterior probabilities on tokens), the feature used to calculate the first-order statis-tics is also extended from MFCC to tandem features and is not necessarily the same feature employed by the tokenizer. Third, the zero-order and first-order statistics vectors are then concate-nated and represented by the simplified supervised i-vector ap-proach followed by the standard back end modeling methods. We study different system setups with different tokens and fea-tures. Finally, selected effective systems are fused at the score level to further improve the performance. Experimental results are reported on the NIST SRE 2010 common condition 5 fe-male part task and the NIST LRE 2007 closed set 30 seconds task for speaker verification and language identification, respec-tively. The proposed generalized i-vector framework outper-forms the i-vector baseline by relatively 45 % in terms of equal error rate (EER) and norm minDCF values. Index Terms: speaker verification, language identification, generalized i-vector, phonetic tokenization, tandem feature