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As the information security on network is being widely concerned,automatic speaker recognition technology could be used to find terror speeches containing some specific speakers.In this paper,we propose an unsupervised approach to improve the performance of an existing NIST-SRE-domain i-vector/PLDA system for the internet speaker verification application with a set of non-speaker-label audios collected from the internet.A speaker factor vector in i-vector space is extracted for each audio with the existing background models.Then a SVM classifier is applied on these speaker factor vectors to do speaker recognition.Speaker factor vectors of those given unlabeled in-domain data are used as the negative samples to train speaker-dependent SVM models.Experiments are conducted on NIST SRE 2010 condition-1,condition-2 task and an internet test-set.Results on the internet test-set shows that the propose approach achieves a relative performance improvement of about 50%in both EER and minDCF over the baseline i-vector/PLDA system.