Elements of information theory
Elements of information theory
Usefulness of the LPC-residue in text-independent speaker verification
Speech Communication
AHUMADA: A large speech corpus in Spanish for speaker characterization and identification
Speech Communication - Speaker recognition and its commercial and forensic applications
Introduction to Linear Optimization
Introduction to Linear Optimization
Speaker Identification Using Harmonic Structure of LP-residual Spectrum
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
On the use of residual cepstrum in speech recognition
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Source separation in post-nonlinear mixtures
IEEE Transactions on Signal Processing
Quasi-nonparametric blind inversion of Wiener systems
IEEE Transactions on Signal Processing
Data Fusion at Different Levels
Multimodal Signals: Cognitive and Algorithmic Issues
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Biometric system performance can be improved by means of data fusion. Several kinds of information can be fused in order to obtain a more accurate classification (identification or verification) of an input sample. In this paper we present a method for computing the weights in a weighted sum fusion for score combinations, by means of a likelihood model. The maximum likelihood estimation is set as a linear programming problem. The scores are derived from a GMM classifier working on different feature extraction techniques. Our experimental results assessed the robustness of the system in front changes on time (different sessions) and robustness in front of changes of microphone. The improvements obtained were significantly better (error bars of two standard deviations) than a uniform weighted sum or a uniform weighted product or the best single classifier. The proposed method scales computationally with the number of scores to be fusioned as the simplex method for linear programming.