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.

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.