Autonomous selection of i-vectors for PLDA modelling in speaker verification

Abstract

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.