Temporally Weighted Linear Prediction Features for Speaker Verification in Additive Noise

摘要

We consider text-independent speaker verification under additive noise corruption. In the popular mel-frequency cepstral coefficient (MFCC) front-end, we substitute the conventional Fourier-based spectrum estimation with weighted linear predictive methods, which have earlier shown success in noise-robust speech recognition. We introduce two temporally weighted variants of linear predictive (LP) modeling to speaker verification and compare them to FFT, which is normally used in computing MFCCs, and to conventional LP. We also investigate the effect of speech enhancement (spectral subtraction) on the system performance with each of the four feature representations. Our experiments on the NIST 2002 SRE corpus indicate that the accuracy of the conventional and proposed features are close to each other on clean data. On 0 dB SNR level, baseline FFT and the better of the proposed features give EERs of 17.4 % and 15.6 %, respectively. These accuracies improve to 11.6 % and 11.2 %, respectively, when spectral subtraction is included as a pre-processing method. The new features hold a promise for noise-robust speaker verification.