Using a New Hybrid Models for Speech and Medical Pattern Classification
Lilia Lazli, Mounir Boukadoum, Abdennasser Chebira, Kurosh Madani
International Journal of Computational Linguistics Research, Vol. 3, No. 1, 2012.
DOI: 
Link: http://www.dline.info/jcl/fulltext/v3n1/2.pdf
Abstract:
The main goal of this paper is to compare the performance which can be achieved by two different hybrid approaches analyzing their applicationsâ€™ 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.