A Comparison of the Lbg, Lvq, Mlp, Som and Gmm Algorithms for Vector Quantisation and Clustering Analysis
نویسندگان
چکیده
We compare the performance of ve algorithms for vector quan-tisation and clustering analysis: the Self-Organising Map (SOM) and Learning Vector Quantization (LVQ) algorithms of Kohonen, the Linde-Buzo-Gray (LBG) algorithm, the MultiLayer Perceptron (MLP) and the GMM/EM algorithm for Gaussian Mixture Models (GMM). We propose that the GMM/EM provides a better representation of the speech space and demonstrate this by comparing the GMM with the LBG, LVQ, MLP and SOM algorithms in phoneme classiication and digit recognition.
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