Locality Sensitive Discriminant Analysis for Speaker Verification

In this paper, we apply Locality Sensitive Discriminant Analysis (LSDA) to speaker verification system for intersession variability compensation. As opposed to LDA which fails to discover the local geometrical structure of the data manifold, LSDA finds a projection which maximizes the margin between i-vectors from different speakers at each local area. Since the number of samples varies in a wide range in each class, we improve LSDA by using adaptive k nearest neighbors in each class and modifying the corresponding within-and between-class weight matrix. In that way, each class has equal importance in LSDA's objective function. Experiments were carried out on the NIST 2010 speaker recognition evaluation (SRE) extended condition 5 female task, results show that our proposed adaptive k nearest neighbors based LSDA method significantly improves the conventional i-vector/ PLDA baseline by 18% relative cost reduction and 28% relative equal error rate reduction.