CONF Roy_ICASSP2010_2010/IDIAP BOOSTED BINARY FEATURES FOR NOISE-ROBUST SPEAKER VERIFICATION Roy, Anindya Magimai-Doss, Mathew Marcel, Sébastien EXTERNAL http://publications.idiap.ch/attachments/papers/2010/Roy_ICASSP2010_2010.pdf PUBLIC 2010 IEEE International Conference on Acoustics, Speech and Signal Processing Dallas, Texas 2010 March 2010 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.