BOOSTED BINARY FEATURES FOR NOISE-ROBUST SPEAKER VERIFICATION
| Type of publication: | Conference paper |
| Citation: | Roy_ICASSP2010_2010 |
| Booktitle: | 2010 IEEE International Conference on Acoustics, Speech and Signal Processing |
| Year: | 2010 |
| Month: | 3 |
| Location: | Dallas, Texas |
| Abstract: | 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. |
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| Projects |
Idiap IM2 MOBIO SNSF-MULTI |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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