Fundamentals of speech recognition
Fundamentals of speech recognition
Deterministic annealing EM algorithm
Neural Networks
SMEM algorithm for mixture models
Proceedings of the 1998 conference on Advances in neural information processing systems II
SMEM Algorithm for Mixture Models
Neural Computation
Mixture density estimation with group membership functions
Pattern Recognition Letters
Pattern Recognition Letters
Modeling coarticulation in EMG-based continuous speech recognition
Speech Communication
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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The EM algorithm for Gaussian mixture models often gets caught in local maxima of the likelihood which involve having too many Gaussians in one part of the space and too few in another, widely separated part of the space. We present a new EM algorithm which performs split and merge operations on the Gaussians to escape from these configurations. This algorithm uses two novel criteria for efficiently selecting the split and merge candidates. Experimental results on synthetic and real data show the effectiveness of using the split and merge operations to improve the likelihood of both the training data and of held-out test data.