Speech recognition system utilizing pre-calculated similarity measurements

Claim 1

A method of preparing a compressed matrix of precalculated distance metrics for comparing an input utterance which is represented by a sequence of prototype data frames selected from a preselected set of prototype data frames with at least some of a vocabulary of word models each of which is represented by a sequence of prototype states selected from a preselected set of prototype states, said method comprising: generating an array of distance metrics for all combinations of prototype frames and prototype states; for each state, identifying the frames for which the corresponding metric is meaningful; for each state, determining a common default value for non-meaningful metrics; for at least one group of frames corresponding to each state, determining the locations within said array containing meaningful metrics; building a combined list of meaningful metrics by adding the meaningful metrics from successive groups of frames using an offset for each group which allows the meaningful metrics for each group to fit into currently unused positions in the list, the relative positions of meaningful metrics within each group being maintained in the list; building an array of said offset values accessed by the corresponding state; building an array distinguishing meaningful and non-meaningful entries in the original array of distance metrics; whereby a measure of match between an input utterance and a vocabulary word model is obtainable by combining corresponding metrics using a default value for non-meaningful metrics and locating respective meaningful metrics in said combined list using said array of offset values.

Claim 2

A method of preparing a compressed matrix of precalculated distance metrics for comparing an input utterance which is represented by a sequence of prototype data frames selected from a preselected set of prototype data frames with at least some of a vocabulary of word models each of which is represented by a sequence of prototype states selected from a preselected set of prototype states, said method comprising: generating an array of distance metrics for all combinations of prototype frames and prototype states; for each state, identifying the frames for which the corresponding metric is meaningful; for each state, determining a common default value for non-meaningful metrics; for at least one group of frames corresponding to each state, determining the locations within said array containing meaningful metrics; building a combined list of meaningful metrics by adding the meaningful metrics from successive groups of frames using an offset for each group which allows the meaningful metrics for each group to fit into currently unused positions in the list, the relative positions of meaningful metrics within each group being maintained in the list; building an array of said offset values accessed by the corresponding state; building an array distinguishing meaningful and non-meaningful entries in the original array of distance metrics; whereby a measure of match between an input utterance and a vocabulary word model is obtainable by combining corresponding metrics using a default value for non-meaningful metrics and locating respective meaningful metrics in said combined list using said array of offset values.

Claim 3

A method of preparing a compressed matrix of precalculated distance metrics for comparing an input utterance which is represented by a sequence of prototype data frames selected from a preselected set of prototype data frames with at least some of a vocabulary of word models each of which is represented by a sequence of prototype states selected from a preselected set of prototype states, said method comprising: generating an array of distance metrics for all combinations of prototype frames and prototype states; for each state, identifying the frames for which the corresponding metric is meaningful; for each state, determining a common default value for non-meaningful metrics; for subgroups of frames corresponding to each state, determining the locations within said array containing meaningful metrics; building a combined list of meaningful metrics by adding the meaningful metrics from successive subgroups of frames using an offset for each subgroup which allows the meaningful metrics for each group to fit into currently unused positions in the list, the relative positions of meaningful metrics within each subgroup being maintained in the list; building an array of said offset values accessed by the corresponding subgroup and state; building an array distinguishing meaningful and non-meaningful entries in the original array of distance metrics; whereby a measure of match between an input utterance and a vocabulary word model is obtainable by combining corresponding metrics using a default value for non-meaningful metrics and locating respective meaningful metrics in said combined list using said array of offset values.

Claim 4

A method of preparing a compressed matrix of precalculated distance metrics for comparing an input utterance which is represented by a sequence of prototype data frames selected from a preselected set of prototype data frames with at least some of a vocabulary of word models each of which is represented by a sequence of prototype states selected from a preselected set of prototype states, said method comprising: generating an array of distance metrics for all combinations of prototype frames and prototype states; for each state, identifying the frames for which the corresponding metric is meaningful; for each state, determining a common default value for non-meaningful metrics; for subgroups of frames corresponding to each state, determining the locations within said array containing meaningful metrics; building a combined list of meaningful metrics by adding the meaningful metrics from successive subgroups of frames using an offset for each subgroup which allows the meaningful metrics for each group to fit into currently unused positions in the list, the relative positions of meaningful metrics within each subgroup being maintained in the list; building an array of said offset values accessed by the corresponding subgroup and state; building an array distinguishing meaningful and non-meaningful entries in the original array of distance metrics; whereby a measure of match between an input utterance and a vocabulary word model is obtainable by combining corresponding metrics using a default value for non-meaningful metrics and locating respective meaningful metrics in said combined list using said array of offset values.

Claim 5

A method of preparing a compressed matrix of precalculated distance metrics for comparing an input utterance which is represented by a sequence of prototype data frames selected from a preselected set of prototype data frames with at least some of a vocabulary of word models each of which is represented by a sequence of prototype states selected from a preselected set of prototype states, said method comprising: generating a matrix of distance metrics for all combinations of prototype frames and prototype states; for each state, order the metrics, best matches first; cumulating the metrics, best matches first, for each frame until a preselected threshold is reached; for each state, assigning a common default value for all metrics which did not contribute to the cumulation; for the frames corresponding to each state, determining the locations within said array containing contributing metrics, said metrics being then considered meaningful; building a combined list of meaningful metrics by adding the meaningful metrics from successive groups of contributing frames using an offset for each group which allows the meaningful metrics for each group to fit into currently unused positions in the list, the relative positions of meaningful metrics within each group being maintained in the list; building an array of said offset values accessed by the corresponding frame and state; building an array distinguishing meaningful and non-meaningful entries in the original array of distance metrics; whereby a measure of match between an input utterance and a vocabulary word model is obtainable by combining corresponding metrics using a default value for non-meaningful metrics and locating respective meaningful metrics in said combined list using said array of offset values.

Claim 6

A method of preparing a compressed matrix of precalculated distance metrics for comparing an input utterance which is represented by a sequence of prototype data frames selected from a preselected set of prototype data frames with at least some of a vocabulary of word models each of which is represented by a sequence of prototype states selected from a preselected set of prototype states, said method comprising: generating a matrix of distance metrics for all combinations of prototype frames and prototype states; for each state, order the metrics, best matches first; cumulating the metrics, best matches first, for each frame until a preselected threshold is reached; for each state, assigning a common default value for all metrics which did not contribute to the cumulation; for the frames corresponding to each state, determining the locations within said array containing contributing metrics, said metrics being then considered meaningful; building a combined list of meaningful metrics by adding the meaningful metrics from successive groups of contributing frames using an offset for each group which allows the meaningful metrics for each group to fit into currently unused positions in the list, the relative positions of meaningful metrics within each group being maintained in the list; building an array of said offset values accessed by the corresponding frame and state; building an array distinguishing meaningful and non-meaningful entries in the original array of distance metrics; whereby a measure of match between an input utterance and a vocabulary word model is obtainable by combining corresponding metrics using a default value for non-meaningful metrics and locating respective meaningful metrics in said combined list using said array of offset values.

Claim 13

A method of preparing a compressed matrix of precalculated distance metrics for comparing an input utterance which is represented by a sequence of prototype data frames selected from a preselected set of prototype data frames with at least some of a vocabulary of word models each of which is represented by a sequence of prototype states selected from a preselected set of prototype states, said method comprising: generating a matrix of distance metrics for all combinations of prototype frames and prototype states, locations within said matrix being addressable by respective state and frame multibit indices; for each state, order the metrics, best matches first; cumulating the metrics, best matches first, for each frame until a preselected threshold is reached; for each state, assigning a common default value for all metrics which did not contribute to the cumulation; for the frames corresponding to each state, determining the locations within said array containing contributing metrics, said metrics being then considered meaningful; dividing the group of frames corresponding to each state into subgroups identifiable by the higher order bits of the frame multibit index; building a combined list of meaningful metrics by adding the meaningful metrics from successive sub-groups of contributing frames, using an offset for each sub-group which allows the meaningful entries to fit into currently unused positions in the list, the relative positions of meaningful metrics within each sub-group being maintained in the list; building an array of said offset values accessed by the corresponding state index and the higher order bits of the frame multibit index; building a check list paralleling said combined list and containing entries distinguishing meaningful and non-meaningful entries in the original array of distance metrics; whereby a measure of match between an input utterance and a vocabulary word model is obtainable by combining corresponding metrics using a default value for non-meaningful metrics and locating respective meaningful metrics in said combined list using said array of offset values.

Claim 14

A method of preparing a compressed matrix of precalculated distance metrics for comparing an input utterance which is represented by a sequence of prototype data frames selected from a preselected set of prototype data frames with at least some of a vocabulary of word models each of which is represented by a sequence of prototype states selected from a preselected set of prototype states, said method comprising: generating a matrix of distance metrics for all combinations of prototype frames and prototype states, locations within said matrix being addressable by respective state and frame multibit indices; for each state, order the metrics, best matches first; cumulating the metrics, best matches first, for each frame until a preselected threshold is reached; for each state, assigning a common default value for all metrics which did not contribute to the cumulation; for the frames corresponding to each state, determining the locations within said array containing contributing metrics, said metrics being then considered meaningful; dividing the group of frames corresponding to each state into subgroups identifiable by the higher order bits of the frame multibit index; building a combined list of meaningful metrics by adding the meaningful metrics from successive sub-groups of contributing frames, using an offset for each sub-group which allows the meaningful entries to fit into currently unused positions in the list, the relative positions of meaningful metrics within each sub-group being maintained in the list; building an array of said offset values accessed by the corresponding state index and the higher order bits of the frame multibit index; building a check list paralleling said combined list and containing entries distinguishing meaningful and non-meaningful entries in the original array of distance metrics; whereby a measure of match between an input utterance and a vocabulary word model is obtainable by combining corresponding metrics using a default value for non-meaningful metrics and locating respective meaningful metrics in said combined list using said array of offset values.

Claim 16

A method of preparing a compressed matrix of precalculated distance metrics for comparing an input utterance which is represented by a sequence of prototype data frames selected from a preselected set of prototype data frames with at least some of a vocabulary of word models each of which is represented by a sequence of prototype states selected from a preselected set of prototype states, said method comprising: generating an array of distance metrics for all combinations of prototype frames and prototype states; designating as meaningful metrics which represent a probability better than a preselected criteria, said criteria being selected to produce meaningful metrics in less than about twenty percent of said entries thereby to generate a sparse matrix of meaningful entries; generating a data structure identifying which metrics in said matrix have been designated as meaningful; generating at least one default value for non-meaningful metrics, each default value being common to a substantial number of said non-meaningful metrics; and compressing said sparse matrix into a packed list comprised essentially entirely of said meaningful entries.

Claim 17

A method of preparing a compressed matrix of precalculated distance metrics for comparing an input utterance which is represented by a sequence of prototype data frames selected from a preselected set of prototype data frames with at least some of a vocabulary of word models each of which is represented by a sequence of prototype states selected from a preselected set of prototype states, said method comprising: generating an array of distance metrics for all combinations of prototype frames and prototype states; designating as meaningful metrics which represent a probability better than a preselected criteria, said criteria being selected to produce meaningful metrics in less than about twenty percent of said entries thereby to generate a sparse matrix of meaningful entries; generating a data structure identifying which metrics in said matrix have been designated as meaningful; generating at least one default value for non-meaningful metrics, each default value being common to a substantial number of said non-meaningful metrics; and compressing said sparse matrix into a packed list comprised essentially entirely of said meaningful entries.