18. The method of claim 15 , wherein said computer is further programmed to execute, and does execute, the following actions: (15) receiving a new sub-audible speech pattern SASP1 uttered by said user, comprising an instance of at least one unknown word, referred to as a “new” word, identified with an index q1, that may be but is not necessarily drawn from said database of Q words; (16) estimating where the new word begins and ends in the new SASP1; (17) providing signal values of the received SASP1 within each of said temporal windows; (18) transforming each of the signal values of the new SASP1, using said Signal Processing Transform (SPT) operation to obtain new SASP1 SPT values, where each SASP1 SPT value is expressed in terms of said at least first and second transform parameters, including a signal frequency and a signal energy associated with the SASP1; (19) providing a second matrix M1 with second matrix entries equal to SPT values for the SASP1, ordered according to each of said at least first and second transform parameters along first and second matrix axes of the second matrix M1; (20) tessellating the matrix M1 into a sequence of exhaustive and mutually exclusive M1-cells that correspond to said sequence of said M-cells for said matrix M where each M1-cell is characterized according to said or more cell criteria for said M-cells; (21) providing, for each M1-cell, an M1-cell representative value depending upon at least one of the second matrix entry values within the M1-cell; (22) formatting the M1-cell representative values as a vector V1 with vector entries v1 k (q1), numbered k=1, . . . , K (K≧2), where q1 refers to said index associated with said new word; (23) analyzing the vector entry values v1 k (q1) using said neural net classifier, having said neural net architecture with said at least one neural net hidden layer, and a sequence w k (q1,opt) of said optimum weight coefficients w k (q1,r1;opt), associated with said at least one neural net hidden layer, and averaged over said R instances (r1=1, . . . , R) of said SASP uttered by said user in claim 15 , (24) providing a neural net output value A1(q1), equal to a sum over the index k of each of the vector entry values v1 k (q1) multiplied by the corresponding averaged optimum weight coefficient value w k (q1,opt); (25) providing a set of neural net reference output values {A1(q′; ref)} as an approximation for the sum A1(q1) for the R1 instances of the SASP1, where q′ is one of said indices corresponding to said database of Q words; (26) forming a comparison difference Δ1(q1,q′)=|A1(q1)} —A(q′; ref) | p , where said quantities A1(q′;ref) and p are determined as in claim 15 ; (27) comparing the difference Δ1(q1,q′) with said first threshold value ε(thr;1). (28) when Δ1(q1,q′) is greater than said first threshold value ε(thr;1), interpreting this condition as indicating that said sub-audible speech pattern SASP1 received is not a sub-audible speech pattern from said database with the corresponding number q1=q′; and (29) when Δ1(q1,q′) is no greater than ε(thr;1), interpreting this condition as indicating that said sub-audible speech pattern SASP1 received is likely to be a sub-audible speech pattern from said database, indexed by q′, with the corresponding index q1.
15. 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 specified sub-audible speech pattern (“SASP”), uttered by the user, and each SASP including at least one word drawn from a selected database of Q words, numbered q=1, . . . , Q (Q≧2); (2) estimating where each SASP begins and ends for each of the signal sequences; (3) providing signal values of the received signal, number r, within a temporal window having a selected window width Δt(win); (4) transforming each of the R SASPs, using an Signal Processing Transform (“SPT”) operation to obtain an SPT value that is expressed in terms of at least one transform parameter having a sequence of parameter values, including a signal frequency an 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 each of 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 the 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 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 with at least one neural net hidden layer, and a sequence of estimated weight coefficient values w k (q,r) associated with that at least one neural net hidden layer, where the neural net classifier provides a sequence of neural net output values A(q,r), equal to a sum over the index k of each of the vector entry values v k (q,r) multiplied by a corresponding weight coefficient value w k (q,r); (10) providing a set of neural net reference output values {A(q; ref)} .as an approximation for the sum A(q,r) for the R instances of the SASP (r=1, . . . , R); (11) forming a difference Δ(q)=Σ r |A(q;r)} A(q;ref)| p , where p is a selected positive exponent (12) comparing the difference Δ(q) with a first threshold value ε(thr;1). (13) when Δ(q) is greater than a first positive threshold value ε(thr;1), adjusting at least one of the weight coefficients w k (q;r), returning to step (9), and repeating the procedures of steps (9)-(12); and (14) when Δ(q) is no greater than ε(thr;1), interpreting this condition as indicating that at least one of an optimum set of weight coefficients {w k (q;r;opt)} has been obtained, and using the 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.