Boosted binary features for noise-robust speaker verification

The standard approach to speaker verification is to extract cepstral features from the speech spectrum and model them by generative or discriminative techniques. We propose a novel approach where a set of client-specific binary features carrying maximal discriminative information specific to the individual client are estimated from an ensemble of pair-wise comparisons of frequency components in magnitude spectra, using Adaboost algorithm. The final classifier is a simple linear combination of these selected features. Experiments on the XM2VTS database strictly according to a standard evaluation protocol have shown that although the proposed framework yields comparatively lower performance on clean speech, it significantly outperforms the state-of-the-art MFCC-GMM system in mismatched conditions with training on clean speech and testing on speech corrupted by four types of additive noise from the standard Noisex-92 database.

• Type: Conference
• Year: 2010
• Where: ICASSP
• Authors: Anindya Roy, Mathew Magimai-Doss, Sébastien Marcel
• Keywords: Clean Speech, Client-specific Binary Features, ICASSP 2010, Maximal Discriminative Information, Signal Processing