Method for symbolic correction in human-machine interfaces
First Claim
1. A method for symbolic correction in human-machine interfaces said method comprising:
- (a) implementing a language model;
(b) implementing a hypothesis model;
(c) implementing an error model; and
(d) processing a symbolic input message using a hardware processor, said processing based on weighted finite-state transducers to encode
1) a set of input hypothesis using said hypothesis model,
2) said language model, and
3) said error model in order to perform correction on said symbolic input message in the human-machine interface employing a probability of transition of the form P(t)=P(LM, EM, HM|t)=P(LM|t)P(EM|t)P(HM|t) with tropical semiring WFSTs (, ⊕
, , 0, 1).
1 Assignment
0 Petitions
Accused Products
Abstract
Disclosed embodiments include methods and systems for symbolic correction in human-machine interfaces that comprise (a) implementing a language model; (b) implementing a hypothesis model; (c) implementing an error model; and (d) processing a symbolic input message based on weighted finite-state transducers to encode 1) a set of input hypothesis using the hypothesis model, 2) the language model, and 3) the error model to perform correction on the sequential pre-segmented symbolic input message in the human-machine interface. According to a particular embodiment, the processing step comprises a combination of the language model, the hypothesis model, and the error model performed without parsing by employing a composition operation between the transducers and a lowest cost path search, exact or approximate, on the composed transducer.
17 Citations
18 Claims
-
1. A method for symbolic correction in human-machine interfaces said method comprising:
-
(a) implementing a language model; (b) implementing a hypothesis model; (c) implementing an error model; and (d) processing a symbolic input message using a hardware processor, said processing based on weighted finite-state transducers to encode
1) a set of input hypothesis using said hypothesis model,
2) said language model, and
3) said error model in order to perform correction on said symbolic input message in the human-machine interface employing a probability of transition of the form P(t)=P(LM, EM, HM|t)=P(LM|t)P(EM|t)P(HM|t) with tropical semiring WFSTs (, ⊕
, ,0 ,1 ). - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
-
-
9. A symbolic correction apparatus for human-machine interfaces comprising:
(a) a memory to store a language model, a hypothesis model, and an error model, and (b) a processor configured for processing a symbolic input message, said processing based on weighted finite-state transducers to encode
1) a set of input hypothesis using said hypothesis model,
2) said language model, and
3) said error model in order to perform correction on said symbolic input message in the human-machine interface employing a probability of transition of the form P(t)=P(LM, EM, HM|t)=P(LM|t)P(EM|t)P(HM|t) with tropical semiring WFSTs (, ⊕
, ,0 ,1 ).- View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
-
17. A non-transitory computer-readable storage medium with an executable program stored thereon to implement symbolic correction in human-machine interfaces, wherein said executable program instructs an apparatus to perform the following steps:
-
(a) implementing a language model; (b) implementing a hypothesis model; (c) implementing an error model; and (d) processing a symbolic input message, said processing based on weighted finite-state transducers to encode
1) a set of input hypothesis using said hypothesis model,
2) said language model, and
3) said error model in order to perform correction on said symbolic input message in the human-machine interface employing a probability of transition of the form P(t)=P(LM, EM, HM|t)=P(LM|t)P(EM|t)P(HM|t) with tropical semiring WFSTs (, ⊕
, ,0 ,1 ). - View Dependent Claims (18)
-
Specification