1. A method for training and using a system to identify a sub-audible signal formed by a source of sub-audible sounds, the method comprising providing a computer that is programmed to execute, and does execute, the following actions:: (1) receiving R signal sequences, numbered r=1,. .. , R (R≧2), with each sequence comprising an instance of a sub-audible speech pattern (“SASP”), uttered by a user, and each SASP including at least one word drawn from a selected database of Q words, numbered q=1,. .. , Q with Q≧2; (2) estimating where each of the R SASPs begins and ends in the sequences; for each of the signal sequences, numbered r=1,. .. , R: (3) providing signal values of a received signal, number r, within a temporal window having a selected window width Δt(win); and (4) transforming each of the R SASPs, using a Signal Processing Transform (“SPT”) operation to obtain an SPT value that is expressed in terms of at least first and second transform parameters comprising at least a signal frequency and a signal energy associated with the SASP; (5) providing a first matrix M with first matrix entries equal to the SPT values for the R SASPs, ordered according to the at least first and second transform parameters along a first matrix axis and along a second matrix axis, respectively, of the matrix M; (6) tessellating the matrix M into a sequence of exhaustive and mutually exclusive cells of matrix entries, referred to as M-cells, with each M-cell containing a collection of contiguous matrix entries, where each M-cell is characterized according to at least one selected M-cell criterion; (7) providing, for each M-cell, an M-cell representative value, depending upon at least one of the first matrix entries within the M-cell; (8) formatting the M-cell representative values as a vector V with vector entry values v k (q;r), numbered k=1,.. . , K (K≧2); (9) analyzing the vector entry values v k (q;r) using a neural net classifier, having a neural net architecture, and a sequence of estimated weight coefficient values associated with at least one of the neural net classifier layers, where the neural net classifier provides a sequence of output values dependent upon the weight coefficient values and upon the vector entry values v k (q; r); (10) receiving the vector entries v k (q;r) and forming a first sum 
 S1(q;r) h =Σ k W 1,k,h (q;r)·v k (q;r), where {w 1,k,h (q;r)}· is a first selected set of adjustable weight coefficients that are estimated by a neural net procedure; (11) forming a first activation function A1{S1(q;r) h }, that is monotonically increasing as the value S1(q;r) h increases; (12) forming a second sum 
 S2(q;r) g =Σ h w 2,h,g (q;r)·A1{ S1(q;r) h } (g =1,. .. , G; G≧1), where w 2,h,g (q;r)· is a second selected set of adjustable weight coefficients that are estimated by the neural net procedure; (13) forming a second activation function A2 {S2(q;r) g } that depends upon the second sum S2(q;r), that is monotonically increasing as the value S2(q;r) increases; (14) providing a set of reference output values {A(q; ref) g } as an approximation for the sum A2 {S2(q,r) g } for the R instances of the SASP; (15) forming a difference Δ1(q)=(1/R·G) Σ r,g |A2{S2(q;r) g }−A](q; ref) g | p1 , where p1 is a selected positive exponent; (16) comparing the difference Δ1(q) with a selected threshold value ε(thr;1); (17) when Δ1(q)[[>]] is greater than ε(thr;1), adjusting at least one of the weight coefficients w 1,k,h (q;r) and the weight coefficients w 2,h,g (q;r), returning to step (10), and repeating the procedures of steps (10)-(16); and (18) when Δ1(q) is no greater than ε(thr;1), interpreting this condition as indicating that at least one of an optimum first set of weight coefficients {w 1,k,h (q;r;opt)} and an optimum second set of weight coefficients {w 2,h,g (q;r;opt)} has been obtained, and using the at least one of the first set and second set of optimum weight coefficients to receive and process a new SASP signal and to estimate whether the received new SASP signal corresponds to a reference word or reference phrase in the selected database.