Nonparametric feature normalization for SVM-based speaker verification

We investigate several feature normalization and scaling approaches for use in speaker verification based on support vector machines. We are particularly interested in methods that are “knowledge-free” and work for a variety of features, leading us to investigate MLLR transforms, phone N-grams, prosodic sequences, and word N-gram features. Normalization methods studied include mean/variance normalization, TFLLR and TFLOG scaling, and a simple nonparametric approach: rank-normalization. We find that rank-normalization is uniformly competitive with other methods, and improves upon them in many cases.

• Type: Conference
• Year: 2008
• Where: ICASSP
• Authors: Andreas Stolcke, Sachin S. Kajarekar, Luciana Ferrer
• Keywords: Feature Normalization, ICASSP 2008, Signal Processing, Support Vector Machines, Word N-gram Features