US5621809A
Expired / Lapsed
US
⏱️ 0.0 years remaining
Computer program product for automatic recognition of a consistent message using multiple complimentary sources of information
Assignee: Cisco Technology Inc
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Filing: 1995-06-07
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Granted: 1997-04-15
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Expires: 2015-06-07
Abstract
Abstract A general approach is provided for the combined use of several sources of information in the automatic recognition of a consistent message. For each message unit (e.g., word) the total likelihood score is assumed to be the weighted sum of the likelihood scores resulting from the separate evaluation of each information source. Emphasis is placed on the estimation of weighing factors used in forming this total likelihood. This method can be applied, for example, to the decoding of a consistent message using both handwriting and speech recognition. The present invention includes three procedures which provide the optimal weighing coefficients.
Inventors
- 👤 Jerome R. Bellegarda
- 👤 Dimitri Kanevsky
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-08 18:31
CPC Classifications
G; G10; G10L; G10L15/00; G10L15/24; G06; G06F; G06F18/00; G06F18/20; G06F18/25; G06F18/254; G06F18/256; G10L15/08; G10L15/10; G10L15/06; G10L15/063; G10L2015/0635
Claims Analysis
Total Claims: 28
Claim 1
Independent Claim
338 words
A computer program product for use with a computer system, comprising: a computer usable medium having computer readable program code means embodied therein for causing a computer to transform a consistent message into a message recognizable by a computer, said computer program product having first computer readable program code means for causing the computer to transform the consistent message generated by a human in at least two formats into electrical signal representations of the consistent message; second computer readable program code means for causing the computer to produce from said electrical signal representations of the consistent message a set of parameters for each said format; third computer readable program code means for causing the computer to generate a likelihood score of recognition from each said set of parameters; fourth computer readable program code means for causing the computer to use said sets of parameters to train a weighting coefficient for each of the at least two formats of the consistent message, wherein said training of a weighting coefficient further comprises partitioning means for partitioning information in each said format into one or more subunits, wherein each subunit corresponds to a piece of the message, grouping means for grouping said subunits from each said format into a plurality of groups, wherein each said subunit in a group corresponds to the same piece of the message, first determining means for determining a likelihood score of recognition for each of said groups of subunits, second determining means for determining a global score for the information based on said likelihood score of recognition, and third determining means for using said global score to determine said trained weighting coefficients; fifth computer readable program code means for causing the computer to generate a weighted expression based on said trained weighting coefficients and said likelihood scores of recognition; and sixth computer readable program code means for causing the computer to select a candidate message unit that maximizes said weighted expression to transform said electrical signal representations of the consistent message into a computer recognizable message.
Claim 2
Dependent Claim
338 words
A computer program product for use with a computer system, comprising: a computer usable medium having computer readable program code means embodied therein for causing a computer to transform a consistent message into a message recognizable by a computer, said computer program product having first computer readable program code means for causing the computer to transform the consistent message generated by a human in at least two formats into electrical signal representations of the consistent message; second computer readable program code means for causing the computer to produce from said electrical signal representations of the consistent message a set of parameters for each said format; third computer readable program code means for causing the computer to generate a likelihood score of recognition from each said set of parameters; fourth computer readable program code means for causing the computer to use said sets of parameters to train a weighting coefficient for each of the at least two formats of the consistent message, wherein said training of a weighting coefficient further comprises partitioning means for partitioning information in each said format into one or more subunits, wherein each subunit corresponds to a piece of the message, grouping means for grouping said subunits from each said format into a plurality of groups, wherein each said subunit in a group corresponds to the same piece of the message, first determining means for determining a likelihood score of recognition for each of said groups of subunits, second determining means for determining a global score for the information based on said likelihood score of recognition, and third determining means for using said global score to determine said trained weighting coefficients; fifth computer readable program code means for causing the computer to generate a weighted expression based on said trained weighting coefficients and said likelihood scores of recognition; and sixth computer readable program code means for causing the computer to select a candidate message unit that maximizes said weighted expression to transform said electrical signal representations of the consistent message into a computer recognizable message.
Claim 3
Dependent Claim
39 words
A computer program product according to claim 1, wherein said trained weighting coefficients are used to produce a new set of parameters and said new set of parameters is applied to generate a new set of trained weighting coefficients.
Claim 4
Dependent Claim
27 words
A computer program product according to claim 2, wherein said new set of parameters is repeatedly produced until a stable set of trained weighting coefficients is isolated.
Claim 5
Dependent Claim
22 words
A computer program product according to claim 1, wherein the consistent message is transformed into said electrical signal representations using a transducer.
Claim 6
Dependent Claim
33 words
A computer program product according to claim 1, wherein said third determining means and said fourth determining means comprise means for generating and training said trained weighting coefficients using a deleted interpolation procedure.
Claim 7
Dependent Claim
91 words
A computer program product according to claim 5, wherein said third determining means comprises: (a) means for initializing said trained weighting coefficients; and (b) means for generating a weight factor for each of the at least two formats of the consistent message, whereby each of said weight factors represent quantitative information measuring the contribution of previously generated weighting coefficients; and wherein said fourth determining means comprises means for generating said trained weighting coefficients based on a normalization of said weight factors for the at least two formats of the consistent message.
Claim 8
Dependent Claim
35 words
A computer program product according to claim 6, further comprising means for iteratively generating said weight factors and said trained weighting coefficients until said trained weighting coefficients stabilize or until a predetermined threshold is met.
Claim 9
Dependent Claim
32 words
A computer program product according to claim 1, wherein said third determining means and said fourth determining means comprise means for generating and training said trained weighting coefficients using an expectation-maximization procedure.
Claim 10
Dependent Claim
110 words
A computer program product according to claim 8, wherein said third determining means comprises: (a) means for initializing a set of expectation probability scores and said trained weighting coefficients for each of the at least two formats of the consistent message; (b) means for generating an expectation probability score based on previously generated expectation probability scores for each of the at least two formats of the consistent message; and (c) means for generating said trained weighting coefficients as the average of said expectation probability scores; and wherein said fourth determining means comprises means for iteratively generating said expectation probabilities and said training weight coefficients until a predetermined threshold is met.
Claim 11
Dependent Claim
34 words
A computer program product according to claim 1, wherein said third determining means and said fourth determining means comprise means for generating and training said trained weighting coefficients using a joint maximum likelihood procedure.
Claim 12
Dependent Claim
193 words
A computer program product according to claim 1, wherein said third determining means comprises: (a) means for producing at least two sets of feature measurements from said at least two formats and organizing said set of measurements into at least two sets of feature vectors; (b) means for providing a warping of said consistent message formed from said at least two formats; (c) means for ordering said set of feature vectors based on some predefined criteria to form an ordered set of feature vectors; (d) means for partitioning said ordered set of feature vectors to form a partitioned set of feature vectors; (e) means for aligning said partitioned set of feature vectors in accordance with said warping step to form an aligned set of feature vectors; (f) means for grouping said aligned set of feature vectors to form a grouped set of feature vectors, wherein each group of units from said grouped set of feature vectors gives rise to likelihood score; and (g) means for producing a global score from said set of likelihood scores; and wherein said fourth determining means comprises means for generating said trained weighting coefficients from said global score.
Claim 13
Dependent Claim
49 words
A computer program product according to claim 11, wherein said third determining means further comprises: means for storing said global score; means for reordering said set of feature vectors; and wherein said fourth determining means further comprises means for generating said training weighting coefficients using said stored global score.
Claim 14
Dependent Claim
26 words
A computer program product according to claim 12, wherein said third determining means further comprises means for determining whether a predetermined set of criteria is met.
Claim 15
Dependent Claim
239 words
A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform steps for transforming a consistent message into a message recognizable by a computer, said method steps comprising: transforming the consistent message generated by a human in at least two formats into electrical signal representations of the consistent message; producing from said electrical signal representations of the consistent message a set of parameters for each said format; generating a likelihood score of recognition from each said set of parameters; using said sets of parameters to train a weighting coefficient for each of the at least two formats of the consistent message, wherein said training comprises partitioning information in each said format into one or more subunits, wherein each subunit corresponds to a piece of the message, grouping said subunits from each said format into a plurality of groups, wherein each said subunit in a group corresponds to the same piece of the message, determining a likelihood score of recognition for each of said groups of subunits, determining a global score for the information based on said likelihood score, and determining said trained weighting coefficients using said global score, generating a weighted expression based on said trained weighting coefficient and said likelihood scores of recognition; and selecting a candidate message unit that maximizes said weighted expressions to transform said electrical signal representations of the consistent message into a computer recognizable message.
Claim 16
Dependent Claim
239 words
A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform steps for transforming a consistent message into a message recognizable by a computer, said method steps comprising: transforming the consistent message generated by a human in at least two formats into electrical signal representations of the consistent message; producing from said electrical signal representations of the consistent message a set of parameters for each said format; generating a likelihood score of recognition from each said set of parameters; using said sets of parameters to train a weighting coefficient for each of the at least two formats of the consistent message, wherein said training comprises partitioning information in each said format into one or more subunits, wherein each subunit corresponds to a piece of the message, grouping said subunits from each said format into a plurality of groups, wherein each said subunit in a group corresponds to the same piece of the message, determining a likelihood score of recognition for each of said groups of subunits, determining a global score for the information based on said likelihood score, and determining said trained weighting coefficients using said global score, generating a weighted expression based on said trained weighting coefficient and said likelihood scores of recognition; and selecting a candidate message unit that maximizes said weighted expressions to transform said electrical signal representations of the consistent message into a computer recognizable message.
Claim 17
Dependent Claim
39 words
A program storage device according to claim 14, wherein said trained weighting coefficients are used to produce a new set of parameters and said new set of parameters is applied to generate a new set of trained weighting coefficients.
Claim 18
Dependent Claim
27 words
A program storage device according to claim 15, wherein said new set of parameters is repeatedly produced until a stable set of trained weighting coefficients is isolated.
Claim 19
Dependent Claim
22 words
A program storage device according to claim 14, wherein the consistent message is transformed into said electrical signal representations using a transducer.
Claim 20
Dependent Claim
22 words
A program storage device according to claim 14, wherein said step of determining said trained weighting coefficients comprises a deleted interpolation procedure.
Claim 21
Dependent Claim
82 words
A program storage device according to claim 18, wherein said deleted interpolation procedure comprises the steps of: (a) initializing said trained weighting coefficients; (b) generating a weight factor for each of the at least two formats of the consistent message, whereby each of said weight factors represent quantitative information measuring the contribution of previously generated weighting coefficients; and (c) generating said trained weighting coefficients based on a normalization of said weight factors for the at least two formats of the consistent message.
Claim 22
Dependent Claim
40 words
A program storage device according to claim 19, wherein steps of generating said weight factors and of generating said trained weighting coefficients are repeated until iterative values of said trained weighting coefficients stabilize or until a predetermined threshold is met.
Claim 23
Dependent Claim
21 words
A program storage device according to claim 14, wherein said step of determining said trained weighting coefficients comprises an expectation-maximization procedure.
Claim 24
Dependent Claim
105 words
A program storage device according to claim 21, wherein said expectation-maximization procedure comprises the steps of: (a) initializing a set of expectation probability scores and said trained weighting coefficients for each of the at least two formats of the consistent message; (b) generating an expectation probability score based on previously generated expectation probability scores for each of the at least two formats of the consistent message; (c) generating said trained weighting coefficients as the average of said expectation probability scores; and (d) repeating said step of generating said expectation probabilities and said step of generating said training weight coefficients until a predetermined threshold is met.
Claim 25
Dependent Claim
23 words
A program storage device according to claim 14, wherein said step of determining said trained weighting coefficients comprises a joint maximum likelihood procedure.
Claim 26
Dependent Claim
175 words
A program storage device according to claim 14, wherein said joint maximum likelihood procedure comprises the steps of: (a) producing at least two sets of feature measurements from said at least two formats and organizing said set of measurements into at least two sets of feature vectors; (b) providing a warping of said consistent message formed from said at least two formats; (c) ordering said set of feature vectors based on some predefined criteria to form an ordered set of feature vectors; (d) partitioning said ordered set of feature vectors to form a partitioned set of feature vectors; (e) aligning said partitioned set of feature vectors in accordance with said warping step to form an aligned set of feature vectors; (f) grouping said aligned set of feature vectors to form a grouped set of feature vectors, wherein each group of units from said grouped set of feature vectors gives rise to likelihood score; (g) producing a global score from said set of likelihood scores; and (h) generating said trained weighting coefficients from said global score.
Claim 27
Dependent Claim
40 words
A program storage device according to claim 24, wherein said joint maximum likelihood procedure further comprises the steps of: storing said global score; reordering said set of feature vectors; and generating said training weighting coefficients using said stored global score.
Claim 28
Dependent Claim
28 words
A program storage device according to claim 25, wherein said joint maximum likelihood procedure further comprises the step of determining whether a predetermined set of criteria is met.
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USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2000-04-14 | 2000-10-14 | 2001-04-15 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2004-04-14 | 2004-10-14 | 2005-04-15 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2008-04-14 | 2008-10-14 | 2009-04-15 | $7700.00 | Window Closed / Lapsed |