Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Learning mixture models using a genetic version of the EM algorithm
Pattern Recognition Letters
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimization of HMM by a Genetic Algorithm
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
Learning from Incomplete Data
Genetic-Based EM Algorithm for Learning Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
On convergence properties of the em algorithm for gaussian mixtures
Neural Computation
MMI training for continuous phoneme recognition on the TIMIT database
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
Clustering in image space for place recognition and visualannotations for human-robot interaction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Experimental Investigation of Three Machine Learning Algorithms for ITS Dataset
FGIT '09 Proceedings of the 1st International Conference on Future Generation Information Technology
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The traditional method for estimation of the parameters of Hidden Markov Model (HMM) based acoustic modeling of speech uses the Expectation-Maximization (EM) algorithm. The EM algorithm is sensitive to initial values of HMM parameters and is likely to terminate at a local maximum of likelihood function resulting in non-optimized estimation for HMM and lower recognition accuracy. In this paper, to obtain better estimation for HMM and higher recognition accuracy, several candidate HMMs are created by applying EM on multiple initial models. The best HMM is chosen from the candidate HMMs which has highest value for likelihood function. Initial models are created by varying maximum frame number in the segmentation step of HMM initialization process. A binary search is applied while creating the initial models. The proposed method has been tested on TIMIT database. Experimental results show that our approach obtains improved values for likelihood function and improved recognition accuracy.