2. The method of claim 1 , wherein said computer is further programmed to execute, and does execute, said step (18) by a procedure comprising the following actions: (19) receiving a new sub-audible speech pattern SASP signal uttered by said user containing an instance of at least one unknown word, referred to as a “new” word, indexed with an index q′ that may be in said database of Q; (20) estimating where the new word begins and ends in the new SASP (21) providing signal values for the new SASP within each of said temporal windows, numbered j=1, . . . , J with J≧2, that are shifted in time relative to each other by selected multiples of a selected displacement time Δt(displ); (22) for the signal values within each of the time-shifted windows, numbered j=1, . . . , J: (23) transforming each of the signal values of the new SASP, using said Signal Processing Transform (SPT) operation to obtain new SASP SPT values with said at least first and second transform SPT values; (24) providing a second matrix M′ with second matrix entries equal to the new SASP SPT values, ordered according to said at least first and second transform parameters along a first and second matrix axes, respectively, of the second matrix M′; (25) tessellating the second matrix M′ into a sequence of exhaustive and mutually exclusive M′-cells that correspond to said M-cells for said tessellated matrix M, where each M′-cell is characterized according to at least one selected M′-cell criterion; (26) providing, for each M′-cell in the second matrix M′, a M′-cell representative value depending upon at least one of the second matrix entries within the M′-cell; (27) formatting the M′-cell representative values as a vector V′ with vector entry values where v′ k (q′;r) refers to new word or phrase index (k=1, . . . , K); (28) applying said neural net classifier and said reference set of said optimum first set and said optimum second set of weight coefficients to compute said neural net classifier output values for each of the time-shifted sequences of the new SASP; (29) receiving the vector entries v′ k (q;r) and forming a first sum 
 S 1′( q′;q″;r ) h =Σ k w′ 1,k,h ( q″;r ;opt)· v′ k ( q′;r ), where weight coefficients w′ 1,k,h (q″;r;opt) are said optimized first weight values coefficients found for a candidate word or phrase (q″) in the database; (30) forming a first new word activation function A1′{S1′(q′;q″;r) h } that depends upon the first sum S1′(q′;q″;r) h ; (31) forming a second sum 
 S 2′( q′;q″;r ) g =Σ h w′ 2,h,g ( q″;r opt)· A 1 ′{S 1′( q′;q″;r ) h }(g=1, . . . .G; G≧1), where weight coefficients w′ 2,h,g (q″;r)· are said optimized second weight coefficients found for a candidate word or phrase (q″) in the database; (32) forming a second new word activation function A2′{S2′(q′;q″;) g } that depends upon the second sum S2′(q′;q″;r) h ; (33) providing a set of reference output values {A′(q″;ref) g } associated with each candidate word or phrase (q″) in the database; (34) forming a comparison difference 
 Δ1′( q″;q′ )=(1/R·G)Σ r,g |A 2 ′{S 2′( q′;q″;r ) g }−A′ ( q ″;ref) g | p2 , where p2 is a selected positive exponent; (35) comparing the difference Δ1(q″;q′) with a selected threshold value ε(thr;2); (36) when the difference Δ1(q″;q′)is greater than ε(thr;2), returning to step (28) and repeating the procedures of steps (28)-(35) with another candidate word or phrase (q″) in the database; and (37) when Δ1(q″;q′)is no greater than ε(thr;2), interpreting this condition as indicating that the present candidate word or phrase (q″) is the “new” word (q′), and indicating that the present candidate word or phrase q″ is likely to be the “new” word q′.
2. The method of claim 1 , wherein said computer is further programmed to execute, and does execute, said step (18) by a procedure comprising the following actions: (19) receiving a new sub-audible speech pattern SASP signal uttered by said user containing an instance of at least one unknown word, referred to as a “new” word, indexed with an index q′ that may be in said database of Q; (20) estimating where the new word begins and ends in the new SASP (21) providing signal values for the new SASP within each of said temporal windows, numbered j=1, . . . , J with J≧2, that are shifted in time relative to each other by selected multiples of a selected displacement time Δt(displ); (22) for the signal values within each of the time-shifted windows, numbered j=1, . . . , J: (23) transforming each of the signal values of the new SASP, using said Signal Processing Transform (SPT) operation to obtain new SASP SPT values with said at least first and second transform SPT values; (24) providing a second matrix M′ with second matrix entries equal to the new SASP SPT values, ordered according to said at least first and second transform parameters along a first and second matrix axes, respectively, of the second matrix M′; (25) tessellating the second matrix M′ into a sequence of exhaustive and mutually exclusive M′-cells that correspond to said M-cells for said tessellated matrix M, where each M′-cell is characterized according to at least one selected M′-cell criterion; (26) providing, for each M′-cell in the second matrix M′, a M′-cell representative value depending upon at least one of the second matrix entries within the M′-cell; (27) formatting the M′-cell representative values as a vector V′ with vector entry values where v′ k (q′;r) refers to new word or phrase index (k=1, . . . , K); (28) applying said neural net classifier and said reference set of said optimum first set and said optimum second set of weight coefficients to compute said neural net classifier output values for each of the time-shifted sequences of the new SASP; (29) receiving the vector entries v′ k (q;r) and forming a first sum 
 S 1′( q′;q″;r ) h =Σ k w′ 1,k,h ( q″;r ;opt)· v′ k ( q′;r ), where weight coefficients w′ 1,k,h (q″;r;opt) are said optimized first weight values coefficients found for a candidate word or phrase (q″) in the database; (30) forming a first new word activation function A1′{S1′(q′;q″;r) h } that depends upon the first sum S1′(q′;q″;r) h ; (31) forming a second sum 
 S 2′( q′;q″;r ) g =Σ h w′ 2,h,g ( q″;r opt)· A 1 ′{S 1′( q′;q″;r ) h }(g=1, . . . .G; G≧1), where weight coefficients w′ 2,h,g (q″;r)· are said optimized second weight coefficients found for a candidate word or phrase (q″) in the database; (32) forming a second new word activation function A2′{S2′(q′;q″;) g } that depends upon the second sum S2′(q′;q″;r) h ; (33) providing a set of reference output values {A′(q″;ref) g } associated with each candidate word or phrase (q″) in the database; (34) forming a comparison difference 
 Δ1′( q″;q′ )=(1/R·G)Σ r,g |A 2 ′{S 2′( q′;q″;r ) g }−A′ ( q ″;ref) g | p2 , where p2 is a selected positive exponent; (35) comparing the difference Δ1(q″;q′) with a selected threshold value ε(thr;2); (36) when the difference Δ1(q″;q′)is greater than ε(thr;2), returning to step (28) and repeating the procedures of steps (28)-(35) with another candidate word or phrase (q″) in the database; and (37) when Δ1(q″;q′)is no greater than ε(thr;2), interpreting this condition as indicating that the present candidate word or phrase (q″) is the “new” word (q′), and indicating that the present candidate word or phrase q″ is likely to be the “new” word q′. 9. The method of claim 2 , wherein said computer is further programmed to execute, and does execute, the following actions: selecting each of said matrix cells to be rectangularity shaped.