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Using a New Hybrid Models for Speech and Medical Pattern Classification

The main goal of this paper is to compare the performance which can be achieved by two different hybrid approaches analyzing their applications' potentiality on real world paradigms (speech recognition and medical diagnosis). We compare the performance obtained with (1) Multinetwork RBF/LVQ structure, we use involves Learning Vector Quantization (LVQ) as a competitive decision processor and Radial Basis Function (RBF) neural models is used as classifier. (2) Hybrid HMM/MLP system using a Multi Layer Perceptron (MLP) to estimate the Hidden Markov Models (HMM) emission probabilities.