Fundamentals of speech recognition
Fundamentals of speech recognition
Speech Communication - Special issue on speech processing in adverse conditions
Comparison of different implementations of MFCC
Journal of Computer Science and Technology
Speaker verification using speaker- and test-dependent fast score normalization
Pattern Recognition Letters
Pattern Recognition Letters
Real-Time Recognition of Spoken Words
IEEE Transactions on Computers
Handbook of Biometrics
Text-independent speaker recognition using graph matching
Pattern Recognition Letters
Front-End Factor Analysis for Speaker Verification
IEEE Transactions on Audio, Speech, and Language Processing
Speaker and Session Variability in GMM-Based Speaker Verification
IEEE Transactions on Audio, Speech, and Language Processing
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We hypothesize that spectral masking may account for most of the gains in robustness against noise using ensemble interval histogram (EIH) and zero crossing with peak amplitude (ZCPA) compared to Mel-frequency cepstral coefficients (MFCCs). To test this hypothesis, we focus on this issue by comparing two MFCC implementations for which the only difference is spectral masking. The comparison involved biometric speaker verification tasks using two publicly available databases. The results confirm the superiority of MFCC with masking, thus corroborating our hypotheses that masking is a key aspect for improved robustness in feature extraction.