Evaluation of MFCC for speaker verification on various windows

Jain, Anjali; Sharma, Om P.

doi:10.1109/icraie.2014.6909144

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…Selecting the optimal window function and improving the front-end process is still an open challenge problem in speech and speaker recognition applications [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Multitaper MFCC and normalized multitaper phase-based features for speaker verification

Mansouri

Castillo-Guerra

2019

SN Appl. Sci.

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One trend of current research activity in speaker verification systems focuses on features that capture complementary information to the Mel-frequency cepstral coefficients (MFCCs). In conventional speaker verification methods, MFCCs are usually computed from a single-tapered DFT spectrum. These methods use the power of the Fourier transform of the time-domain speech frames and ignore the phase component due to its large amount of uncertainty. The multitaper phase information has been also ignored when the multitaper MFCC method applies. We propose a phase information extraction method that normalizes the change variation in the multitaper phase. The goal of this work is to incorporate phase information using low-variance multitaper spectrum estimation method instead of conventional single-taper window and normalizes the change variation in multitaper phase according to the frame position of the input speech to reduce the uncertainty of multitaper phase information in both the state-of-the-art Gaussian mixture model-universal background model (GMM-UBM) baseline and the i-vector speaker verification system. The experiments are conducted on the TIMIT database for both the clean and noisy conditions (SNR = 10 dB). The multitaper inverted-MFCC is also added to the overall system to produce diversity with respect to the fused subsystem. The results show relative improvement of 34% and 25% in terms of equal error rate (EER) over the MFCC baseline in clean and noisy conditions for GMM-UBM system, respectively. The relative improvement in terms of EER with i-vector back-end are about 36% and 16% in clean and noisy conditions as well.

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“…Selecting the optimal window function and improving the front-end process is still an open challenge problem in speech and speaker recognition applications [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%