Automatic gender identification by speech signal using eigenfiltering based on Hebbian learning

This work presents an automatic gender identification algorithm based on eigenfiltering. A maximum eigenfilter is implemented by means of an artificial neural network (ANN) trained via generalized Hebbian learning. The eigenfilter uses the principal component analysis to perform maximum information extraction from the speech signal, which enhances correlated information and improves the pattern analysis. Also, a well known speech processing technique is applied, the mel-frequency cepstral coefficients. This technique is a classical approach for speech feature extraction, and it is a very efficient way to represent physiological voice parameters. The pattern classification uses a radial basis function neural network. Experimental results have shown that the identification algorithm overall performance was widely increased by the eigenfiltering process.