Journal Article Autonomous Selection of i-Vectors for PLDA Modelling in Speaker Verification

ビスワス, サンギータ  ,  BISWAS, SANGEETA  ,  ヨハン, ロダン  ,  Rohdin, Johan  ,  篠田, 浩一  ,  Shinoda, Koichi

72pp.32 - 46 , 2015-05 , Elsevier
Recently, systems combining i-vector and probabilistic linear discriminant analysis (PLDA) have become one of the state-of-the-art methods in text-independent speaker verification. The training data of a PLDA model is often collected from a large, diverse population. However, including irrelevant or noisy training data may deteriorate the verification performance. In this paper, we first show that data selection using k-NN improves the speaker verification performance. We then present a robust way of selecting k based on the local distance-based outlier factor (LDOF). We call this method flexible k-NN (fk-NN). We conduct experiments on male and female trials of several telephone conditions of the NIST 2006, 2008, 2010 and 2012 Speaker Recognition Evaluations (SRE). By using fk-NN, we discard a substantial amount of irrelevant or noisy training data without depending on tuning k, and achieve significant performance improvements on the NIST SRE sets.
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