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.


Presented at:
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, Dallas, Texas
Year:
2010
Laboratories:




 Record created 2010-02-11, last modified 2018-03-17

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