A comparison of speech feature extraction employing autonomous neural network topologies
J.M. Llamas Elvira, F.J. Dickin, R.A. Carrasco · 1991
Describes results obtained from an experimental speech recognition system designed to assess the suitability of several different types of neural network when used for feature extraction. A number of independent speech samples were acquired using a commercial system (Micro Speech Laboratory) at a sampling rate of 10 kHz and encoded into 10 data-bits per sample. The data was further factorized by three common algorithms in order to extract alternative characteristics of feature structure, namely: (a) a 12-parameter fast-Fourier transform (FFT); (b) a 12-parameter FFT in association with the mean energy value per sample frame; and (c) a 12-parameter linear predictive coding (LPC) Cholesky-based method. The data obtained from these three factorizations was used to train each of the following neural network topologies: (a) Adaline; (b) Perceptron; and (c) Back-propagation.