Distributed pattern recognition training method and system
First Claim
Patent Images
1. A distributed pattern recognition training method, comprising:
- providing data communication between at least one central pattern analysis node and a plurality of peripheral data analysis sites;
communicating, from said at least one central pattern analysis node to said plurality of peripheral data analysis sites, a plurality of kernel-based pattern elements; and
performing a plurality of iterations of pattern template training at each of said plurality of peripheral data analysis sites.
1 Assignment
0 Petitions
Accused Products
Abstract
A distributed pattern recognition training method includes providing data communication between at least one central pattern analysis node and a plurality of peripheral data analysis sites. The method also includes communicating from the at least one central pattern analysis node to the plurality of peripheral data analysis a plurality of kernel-based pattern elements. The method further includes performing a plurality of iterations of pattern template training at each of the plurality of peripheral data analysis sites.
166 Citations
60 Claims
-
1. A distributed pattern recognition training method, comprising:
-
providing data communication between at least one central pattern analysis node and a plurality of peripheral data analysis sites;
communicating, from said at least one central pattern analysis node to said plurality of peripheral data analysis sites, a plurality of kernel-based pattern elements; and
performing a plurality of iterations of pattern template training at each of said plurality of peripheral data analysis sites. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 45, 46, 47, 48, 49, 50, 51, 52, 53)
-
-
21. A speech recognition method comprising:
-
performing a base speech recognition process which generates utterance hypotheses;
obtaining a representation of a set of event types which may occur in utterance hypotheses generated by said base speech recognition process;
obtaining a plurality of hypothesized event pair discrimination models;
obtaining a plurality of utterance hypotheses from said base speech recognition process;
selecting at least one pair from said plurality of utterance hypotheses;
for each selected pair of utterance hypotheses, selecting at least one point in time such that within a specified interval of said point in time a first hypothesized particular event happens according to a first hypothesis of said pair of utterance hypotheses and a second hypothesized particular event happens according to a second hypothesis of said pair of utterance hypotheses;
obtaining data observations at said at least one point in time;
rescoring said pair of utterance hypotheses based at least in part on said event pair discrimination models and said data observations at said at least one point in time; and
re-ranking said plurality of hypotheses based on said rescoring of said selected at least one pair from said plurality of hypotheses. - View Dependent Claims (22, 23, 24, 25, 26, 27, 28, 29, 30)
-
-
31. A two stage speech recognition method, comprising:
-
obtaining a base recognition process which generates utterance hypotheses;
obtaining a representation of the set of event types which might occur in utterance hypotheses generated by said base speech recognition process; and
obtaining a plurality of self-normalizing event detection verification models trained at least in part on errors made by said base speech recognition system;
obtaining a plurality of hypotheses from said base speech recognition system;
for each of said plurality of hypotheses, obtaining a list of hypothesized events which happen according to said hypothesis and the hypothesized time at which each of said events occurs;
rescoring each of said plurality of hypotheses by adding the output score from the event detection verification model for each event in said list of hypothesized events; and
re-ranking said plurality of hypotheses based on said rescoring and basing the output of said two stage speech recognition method on said re-ranking. - View Dependent Claims (32, 33, 34, 35)
-
-
36. A pattern scoring method, comprising:
-
obtaining a plurality of template data items;
obtaining a plurality of kernel functions;
for each of said plurality of template data items, creating a plurality of functionals where each particular functional is associated with a particular template data item and a particular kernel function;
computing the score for each sample data item based on the value of a linear combination of a subset of said plurality of functionals; and
for each pattern to be scored, selecting the particular functionals to be used and the weight to be given to each particular functional based on a constrained optimization problem which minimizes a function of the weights for a given amount of separation between the pattern classes. - View Dependent Claims (37, 38, 39, 40, 41, 42, 43, 44)
-
-
54. A program product having machine-readable program code for performing distributed pattern recognition training, the program code, when executed, causing a machine to perform the following steps:
-
providing data communication between at least one central pattern analysis node and a plurality of peripheral data analysis sites;
communicating, from said at least one central pattern analysis node to said plurality of peripheral data analysis sites, a plurality of kernel-based pattern elements; and
performing a plurality of iterations of pattern template training at each of said plurality of peripheral data analysis sites. - View Dependent Claims (55, 56, 57, 58, 59, 60)
-
Specification