Floating search methods in feature selection
Pattern Recognition Letters
Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
American Journal of Mathematical and Management Sciences - Special issue: modern digital simulation methodology, II
A fast fixed-point algorithm for independent component analysis
Neural Computation
Proceedings of the 5th international conference on Multimodal interfaces
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Discrete-time speech signal processing: principles and practice
Discrete-time speech signal processing: principles and practice
Enhanced VQ-based algorithms for speech independent speaker identification
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
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This study employs independent component analysis (ICA) subspace feature selection for the robust speaker verification (SV). ICA subspace provides statistically independent basis that spans the same space and preserves the Euclidean distance measurements. These independent components are applied to a vector quantizer (VQ) SV system. In the feature space modification stage, a batch-mode FastICA algorithm and two adaptive algorithms EGLD-ICA and Pearson-ICA are employed for two-microphone case. As a result, the feature space is modified by a choice of independent component basis to obtain a lower classification error and a better generalization in real environments. The performance of the approach is demonstrated with YOHO database in various noise cases.