Nonparametric feature normalization for SVM-based speaker verification

ANDREAS STOLCKE; SACHIN S. KAJAREKAR; LUCIANA FERRER

Las Vegas

Congreso; IEEE Conference on Acoustics, Speech, and Signal Processing (ICASSP); 2008

IEEE

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 includemean/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.