Title :
Speaker-independent speech recognition by means of functional-link neural networks
Author :
de Arriaga, F. ; El Alami, M.
Abstract :
The purpose of the paper is to show the improvements so far obtained in the solution of the speech recognition problem by means of functional-link neural networks. The specific problem deals with speaker independent recognition of Spanish vowels, a problem of theoretical and practical complexity. The voice Fourier spectrum has been obtained and from it the mel-cepstrum and cepstrum coefficients, which have been used as inputs for the neural architecture in charge of the recognition problem. Different polynomial functions such as Newton and Lagrange developments are used as functional expansions of the network. The results so far obtained show improvements in the recognition rate in all cases with respect to those obtained with the multilayer perceptron
Keywords :
Newton method; learning (artificial intelligence); polynomials; speech recognition; Spanish vowels; cepstrum coefficients; functional-link neural networks; mel-cepstrum coefficients; polynomial functions; speaker-independent speech recognition; voice Fourier spectrum; Cepstrum; Character recognition; Fourier transforms; Frequency domain analysis; Lagrangian functions; Multilayer perceptrons; Neural networks; Polynomials; Speech recognition; Stability;
Conference_Titel :
Pattern Recognition, 2000. Proceedings. 15th International Conference on
Conference_Location :
Barcelona
Print_ISBN :
0-7695-0750-6
DOI :
10.1109/ICPR.2000.906247
