The performance of automatic speaker recognition systems is significantly degraded by acoustic mismatches between training and testing conditions. Such acoustic mismatches are commonly encountered in systems that operate on speech collected over telephone networks, where different handsets and different network routes impose varying convolutional distortions on the speech signal. A new algorithm, the modified-mean cepstral mean normalization with frequency warping (MMCMNFW) method, which improves upon the commonly-employed cepstral mean subtraction method, has been developed. Experimental results on closed-set speaker identification tasks on a channel-corrupted subset of the TIMIT database and on a subset of the NTIMIT database are presented. The new algorithm is shown to offer improved recognition rates over other existing channel normalization methods on these databases.
['Alvin Garcia', 'Richard J. Mammone']
Channel-robust speaker identification using modified-mean cepstral mean normalization with frequency warping