Introduction to the theory of neural computation
Introduction to the theory of neural computation
Robust Clustering with Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
ACM Computing Surveys (CSUR)
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
SMEM Algorithm for Mixture Models
Neural Computation
Face recognition/detection by probabilistic decision-based neural network
IEEE Transactions on Neural Networks
User adaptive handwriting recognition by self-growing probabilistic decision-based neural networks
IEEE Transactions on Neural Networks
Capture interspeaker information with a neural network for speaker identification
IEEE Transactions on Neural Networks
Constructing and application of multimedia TV-news archives
Expert Systems with Applications: An International Journal
Sequential EM for Unsupervised Adaptive Gaussian Mixture Model Based Classifier
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Model-based clustering by probabilistic self-organizing maps
IEEE Transactions on Neural Networks
Learning the number of Gaussian cusing hypothesis test
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Constructing and application of multimedia TV news archives
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Spoken language identification for Indian languages using split and merge EM Algorithm
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
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We propose a self-splitting Gaussian mixture learning (SGML) algorithm for Gaussian mixture modelling. The SGML algorithm is deterministic and is able to find an appropriate number of components of the Gaussian mixture model (GMM) based on a self-splitting validity measure, Bayesian information criterion (BIC). It starts with a single component in the feature space and splits adaptively during the learning process until the most appropriate number of components is found. The SGML algorithm also performs well in learning the GMM with a given component number. In our experiments on clustering of a synthetic data set and the text-independent speaker identification task, we have observed the ability of the SGML for model-based clustering and automatically determining the model complexity of the speaker GMMs for speaker identification.