It is possible today to automate a growing number of speaker-recognition tasks with such technologies as speaker verification and speaker identification. Like human listeners, voice biometrics use the features of a person’s voice to ascertain the speaker’s identity. Systems performing this function have been applied to real-world security applications for more than a decade. Their use is increa...

We propose a practical, feature-level and score-level fusion approach by combining acoustic and estimated articulatory information for both text independent and text dependent speaker verification. From a practical point of view, we study how to improve speaker verification performance by combining dynamic articulatory information with the conventional acoustic features. On text independent spe...

The technique of transforming voices in order to hide the real identity a speaker is called voice disguise, among which automatic disguise (AVD) by modifying spectral and temporal characteristics with miscellaneous algorithms are easily conducted softwares accessible public. AVD has posed great threat both human listening verification (ASV). In this paper, we have found that ASV not only victim...

— The two major applications of speaker recognition applications are speaker verification and speaker identification. But in most of the cases the signal is corrupted with background interferences such as noise and echo. This paper proposes the method of speaker recognition and identification after the noise separation and echo cancellation. Support vector machine(svm) classification based sign...

In this paper, we investigate the use of the Wavelet Transform for text-dependent and text-independent Speaker Verification tasks. We have introduced a Principal Component Analysis based wavelet transform to perform frequencies segmentation with levels decomposition. A speaker dependent library tree has been built, corresponding to the best structure for a given speaker. The constructed tree is...

We study the problem of vocal effort mismatch in speaker verification. Changes in speaker’s vocal effort induce changes in fundamental frequency (F0) and formant structure which introduce unwanted intra-speaker variations to features. We compare seven alternative spectrum estimators in the context of melfrequency cepstral coefficient (MFCC) extraction for speaker verification. The compared vari...

The maximum likelihood estimation (MLE) and Bayesian maximum a-posteriori (MAP) adaptation methods for Gaussian mixture models (GMM) have proven to be effective and efficient for speaker verification, even though each speaker model is trained using only his own training utterances. Discriminative criteria aim at increasing discriminability by using out-of-class data. In this paper, we consider ...