Efficient Speaker Recognition Using Approximated Cross Entropy (ACE)
IEEE Transactions on Audio, Speech, and Language Processing
Analysis of Feature Extraction and Channel Compensation in a GMM Speaker Recognition System
IEEE Transactions on Audio, Speech, and Language Processing
Real-time speaker identification and verification
IEEE Transactions on Audio, Speech, and Language Processing
Unsupervised Discriminative Training With Application to Dialect Classification
IEEE Transactions on Audio, Speech, and Language Processing
An overview of text-independent speaker recognition: From features to supervectors
Speech Communication
Particle swarm optimization aided orthogonal forward regression for unified data modeling
IEEE Transactions on Evolutionary Computation
A hybrid particle swarm optimization approach to bernoulli mixture models
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
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Recently, we introduced the sorted Gaussian mixture models (SGMMs) algorithm providing the means to tradeoff performance for operational speed and thus permitting the speed-up of GMM-based classification schemes. The performance of the SGMM algorithm depends on the proper choice of the sorting function, and the proper adjustment of its parameters. In the present work, we employ particle swarm optimization (PSO) and an appropriate fitness function to find the most advantageous parameters of the sorting function. We evaluate the practical significance of our approach on the text-independent speaker verification task utilizing the NIST 2002 speaker recognition evaluation (SRE) database while following the NIST SRE experimental protocol. The experimental results demonstrate a superior performance of the SGMM algorithm using PSO when compared to the original SGMM. For comprehensiveness we also compared these results with those from a baseline Gaussian mixture model-universal background model (GMM-UBM) system. The experimental results suggest that the performance loss due to speed-up is partially mitigated using PSO-derived weights in a sorted GMM-based scheme.