Temporally Weighted Linear Prediction Features for Tackling Additive Noise in Speaker Verification

Text-independent speaker verification under additive noise corruption is considered. In the popular mel-frequency cepstral coefficient (MFCC) front-end, the conventional Fourier-based spectrum estimation is substituted with weighted linear predictive methods, which have earlier shown success in noise-robust speech recognition. Two temporally weighted variants of linear predictive modeling are introduced to speaker verification and they are compared to FFT, which is normally used in computing MFCCs, and to conventional linear prediction. The effect of speech enhancement (spectral subtraction) on the system performance with each of the four feature representations is also investigated. Experiments by the authors on the NIST 2002 SRE corpus indicate that the accuracy of the conventional and proposed features are close to each other on clean data. For factory noise at 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 preprocessing method. The new features hold a promise for noise-robust speaker verification.