1. A method of training a neural network used in a speech-recognition system, said neural network comprising a plurality of neurons, said method comprising the following steps: receiving an example spoken word; performing analog-to-digital conversion of said example spoken word to produce a digitized signal; performing cepstral analysis of said digitized signal to produce a sequence of data frames; generating a plurality of data blocks from said sequence of data frames; determining if said neural network is to recognize said example spoken word; (i) if so, equating neural network output to one; (ii) if not, equating said neural network output to zero; and training said neural network by calculating a plurality of weight values for said neural network, wherein if the number of said neurons is equal to the number of said data blocks, a matrix-inversion technique is used to calculate said weight values, otherwise, a least-squares estimation technique is used to calculate said weight values.
1. A method of training a neural network used in a speech-recognition system, said neural network comprising a plurality of neurons, said method comprising the following steps: receiving an example spoken word; performing analog-to-digital conversion of said example spoken word to produce a digitized signal; performing cepstral analysis of said digitized signal to produce a sequence of data frames; generating a plurality of data blocks from said sequence of data frames; determining if said neural network is to recognize said example spoken word; (i) if so, equating neural network output to one; (ii) if not, equating said neural network output to zero; and training said neural network by calculating a plurality of weight values for said neural network, wherein if the number of said neurons is equal to the number of said data blocks, a matrix-inversion technique is used to calculate said weight values, otherwise, a least-squares estimation technique is used to calculate said weight values. 20. The method recited in claim 1, wherein said least-squares estimation technique utilizes a pseudo-inverse algorithm.