Performance of Biometric Quality Measures
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Audio, Speech, and Language Processing
NIST Speaker Recognition Evaluations Utilizing the Mixer Corpora—2004, 2005, 2006
IEEE Transactions on Audio, Speech, and Language Processing
A Study of Interspeaker Variability in Speaker Verification
IEEE Transactions on Audio, Speech, and Language Processing
P.563—The ITU-T Standard for Single-Ended Speech Quality Assessment
IEEE Transactions on Audio, Speech, and Language Processing
A Comparative Study of Fingerprint Image-Quality Estimation Methods
IEEE Transactions on Information Forensics and Security
Quality-based conditional processing in multi-biometrics: application to sensor interoperability
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Speaker verification in score-ageing-quality classification space
Computer Speech and Language
| Hi-index | 0.00 |
In this work, we analyze several quality measures for speaker verification from the point of view of their utility, i.e., their ability to predict performance in an authentication task. We select several quality measures derived from classic indicators of speech degradation, namely ITU P.563 estimator of subjective quality, signal to noise ratio and kurtosis of linear predictive coefficients. Moreover, we propose a novel quality measure derived from what we have called Universal Background Model Likelihood (UBML), which indicates the degradation of a speech utterance in terms of its divergence with respect to a given universal model. Utility of quality measures is evaluated following the protocols and databases of NIST Speaker Recognition Evaluation (SRE) 2006 and 2008 (telephone-only subset), and ultimately by means of error-vs.-rejection plots as recommended by NIST. Results presented in this study show significant utility for all the quality measures analyzed, and also a moderate decorrelation among them.