Method of partitioning a sequence of data frames

Claim 2

In a pattern-recognition system, a method for partitioning a sequence of data frames into a sequence of data blocks, said method comprising the following steps: (a) converting a spoken word into a signal representing said sequence of data frames; (b) receiving a first data frame of said sequence of data frames; (c) equating a current data frame to said first data frame; (d) equating a current data block to a first data block of said sequence of data blocks; (e) assigning said current data frame to said current data block; (f) determining whether there is a next data frame in said sequence of data frames; (i) if so, proceeding to step (g); (ii) if not, concluding said method; (g) equating said current data frame to said next data frame; (h) equating said current data block to a next data block in said sequence of data blocks; (j) assigning said current data frame to said current data block; (k) determining if said current data block is a last one of said sequence of data blocks; (i) if so, proceeding to step (l); (ii) if not, returning to step (f); and (l) equating said next data block to said first data block, and returning to step (j).

Claim 3

The method recited in claim 1, further comprising the following step: including a plurality of data points based on cepstrum coefficients in at lease one of said sequence of data frames.

Claim 4

The method recited in claim 1, wherein said method said is implemented using at least one integrated circuit.

Claim 5

The method recited in claim 1, wherein said method is implemented using a sequence of computer-executable instructions.

Claim 6

In pattern-recognition system, a method for generating a sequence of data blocks, said method comprising the following steps: (a) converting a spoken word into a signal representing a sequence of data frames; (b) receiving a first data frame of said sequence of data frames; (c) equating a current data frame to said first data frame; (d) equating a current data block to a first data block of said sequence of data blocks; (e) assigning said current data frame to said current data block; (f) determining whether there is a next data frame in said sequence of data frames; (i) if so, proceeding to step (g); (ii) if not, proceeding to step (m); (g) equating said current data frame too said next data frame; (h) equating said current data block to a next data block in said sequence of data blocks; (j) assigning said current data frame to said current data block; (k) determining if said current data block is a last one of said sequence of data blocks; (i) if so, proceeding to step (l); (ii) if not, returning to step (f); (l) equating said next data block to said first data block, and returning to step (j); and (m) determining if said current data block is said last one of said sequence of data blocks; if so, concluding said method, but if not, copying said current data frame to each of said sequence of data blocks following said current data block.

Claim 7

In pattern-recognition system, a method for generating a sequence of data blocks, said method comprising the following steps: (a) converting a spoken word into a signal representing a sequence of data frames; (b) receiving a first data frame of said sequence of data frames; (c) equating a current data frame to said first data frame; (d) equating a current data block to a first data block of said sequence of data blocks; (e) assigning said current data frame to said current data block; (f) determining whether there is a next data frame in said sequence of data frames; (i) if so, proceeding to step (g); (ii) if not, proceeding to step (m); (g) equating said current data frame too said next data frame; (h) equating said current data block to a next data block in said sequence of data blocks; (j) assigning said current data frame to said current data block; (k) determining if said current data block is a last one of said sequence of data blocks; (i) if so, proceeding to step (l); (ii) if not, returning to step (f); (l) equating said next data block to said first data block, and returning to step (j); and (m) determining if said current data block is said last one of said sequence of data blocks; if so, concluding said method, but if not, copying said current data frame to each of said sequence of data blocks following said current data block.

Claim 8

The method recited in claim 5, further comprising the following step: including a plurality of data points based on cepstrum coefficients in at least one of said sequence of data frames.

Claim 9

The method recited in claim 5, wherein said method is implemented using at least one integrated circuit.

Claim 10

The method recited in claim 5, wherein said method is implemented using a sequence of computer-executable instructions.

Claim 11

In a speech-recognition system, a method for generating a sequence of data blocks, said method comprising the following steps: (a) converting a spoken word to an electrical signal; (b) performing an A/D conversion of said electrical signal to produce a digitized signal; (c) performing cepstral analysis of said digitized signal to produce a sequence of data frames, wherein each of said sequence of data frames includes a plurality of data points based on cepstrum coefficients; (d) equating a current data frame to a first data frame of said sequence of data frames; (e) equating a current data block to a first data block of said sequence of data blocks; (f) assigning said current data frame to said current data block; (g) determining whether there is a next data frame in said sequence of data frames; (i) if so, proceeding to step (h); (ii) if not, concluding said method; (h) equating said current data frame to said next data frame; (j) equating said current data block to a next data block in said sequence of data blocks; (k) assigning said current data frame to said current data block; (l) determining if said current data block is a last one of said sequence of data blocks; (i) if so, proceeding to step (m); (ii) if not, returning to step (g); and (m) equating said next data block to said first data block, and returning to step (g).

Claim 12

In a speech-recognition system, a method for generating a sequence of data blocks, said method comprising the following steps: (a) converting a spoken word to an electrical signal; (b) performing an A/D conversion of said electrical signal to produce a digitized signal; (c) performing cepstral analysis of said digitized signal to produce a sequence of data frames, wherein each of said sequence of data frames includes a plurality of data points based on cepstrum coefficients; (d) equating a current data frame to a first data frame of said sequence of data frames; (e) equating a current data block to a first data block of said sequence of data blocks; (f) assigning said current data frame to said current data block; (g) determining whether there is a next data frame in said sequence of data frames; (i) if so, proceeding to step (h); (ii) if not, concluding said method; (h) equating said current data frame to said next data frame; (j) equating said current data block to a next data block in said sequence of data blocks; (k) assigning said current data frame to said current data block; (l) determining if said current data block is a last one of said sequence of data blocks; (i) if so, proceeding to step (m); (ii) if not, returning to step (g); and (m) equating said next data block to said first data block, and returning to step (g).

Claim 13

The method recited in claim 9, wherein said sequence of frames represents a speech sample.

Claim 14

The method recited in claim 9 wherein step (a) comprises the following sub-step: sampling said spoken word at fixed intervals of time to generate said electrical signal.

Claim 15

The method recited in claim 9 wherein each frame in said sequence of frames comprises a plurality of data points, wherein each of said data points represents the value of a function which is based on cepstrum coefficients.

Claim 16

The method recited in claim 9, wherein said method is implemented using at least one integrated circuit.

Claim 17

The method recited in claim 9, wherein said method is implemented using a sequence of computer-executable instructions.

Claim 18

The method recited in claim 9, wherein said sequence of data frames has a finite number of data frames and said sequence of data blocks has a finite length, and said method further comprises the following steps: determining said finite number of data frames; defining a number of data frames per data block; and calculating said finite length of said sequence of data blocks according to said number of data frames and said number of data frames per data block.

Claim 19

The method of claim 15, wherein the step of calculating includes the following sub-steps: dividing said finite number of data frames by said data frames per data block to produce a result; and rounding said result to a nearest integer greater than said result to produce said finite length of said sequence of data blocks.

Claim 20

In a speech-recognition system, a method for generating a sequence of data blocks having a finite length, said method comprising the following steps: (a) converting a spoken word to an electrical signal; (b) performing an A/D conversion of said electrical signal to produce a digitized signal; (c) performing cepstral analysis of said digitized signal to produce a sequence of data frames, wherein said sequence of data frames has a finite number of data frames and each of said sequence of data frames includes a plurality of data points based on cepstrum coefficients, each of the plurality of data points being a 32-bit digital word; (d) determining said finite number of data frames; (e) defining a number of data frames per data block; (f) calculating said finite length of said sequence of data blocks according to said number of data frames and said number of data frames per data block; (g) equating a current data frame to a first data frame of said sequence of data frames; (h) equating a current data block to a first data block of said sequence of data blocks; (j) assigning said current data frame to said current data block; (k) determining whether there is a next data frame in said sequence of data frames; (i) if so, proceeding to step (l); (ii) if not, proceeding to step (g); (l) equating said current data frame to said next data frame; (m) equating said current data block to a next data block in said sequence of data blocks; (n) assigning said current data frame to said current data block; (o) determining if said current data block is a last one of said sequence of data blocks; (i) if so, proceeding to step (p); (ii) if not, returning to step (k); and (p) equating said next data block to said first data block, and returning to step (k); and (q) determining if said current data block is said last one of said sequence of data blocks; if so, concluding said method, but if not, copying said current data frame to each of said sequence of data blocks following said current data block.

Claim 21

In a speech-recognition system, a method for generating a sequence of data blocks having a finite length, said method comprising the following steps: (a) converting a spoken word to an electrical signal; (b) performing an A/D conversion of said electrical signal to produce a digitized signal; (c) performing cepstral analysis of said digitized signal to produce a sequence of data frames, wherein said sequence of data frames has a finite number of data frames and each of said sequence of data frames includes a plurality of data points based on cepstrum coefficients, each of the plurality of data points being a 32-bit digital word; (d) determining said finite number of data frames; (e) defining a number of data frames per data block; (f) calculating said finite length of said sequence of data blocks according to said number of data frames and said number of data frames per data block; (g) equating a current data frame to a first data frame of said sequence of data frames; (h) equating a current data block to a first data block of said sequence of data blocks; (j) assigning said current data frame to said current data block; (k) determining whether there is a next data frame in said sequence of data frames; (i) if so, proceeding to step (l); (ii) if not, proceeding to step (g); (l) equating said current data frame to said next data frame; (m) equating said current data block to a next data block in said sequence of data blocks; (n) assigning said current data frame to said current data block; (o) determining if said current data block is a last one of said sequence of data blocks; (i) if so, proceeding to step (p); (ii) if not, returning to step (k); and (p) equating said next data block to said first data block, and returning to step (k); and (q) determining if said current data block is said last one of said sequence of data blocks; if so, concluding said method, but if not, copying said current data frame to each of said sequence of data blocks following said current data block.

Claim 22

The method recited in claim 17, wherein step (f) includes the following sub-steps: dividing said finite number of data frames by said data frames per data block to produce a result; and rounding said result to a nearest integer greater than said result to generate said finite length of said sequence of data blocks.

Claim 23

The method recited in claim 17, wherein step (a) comprises the following sub-step: sampling said spoken word at fixed intervals of time to generate said electrical signal.

Claim 24

The method for partitioning a sequence of data frames recited in claim 17, wherein said method is implemented using at least one integrated circuit.

Claim 25

The method recited in claim 17, wherein said method is implemented using a sequence of computer-executable instructions.