| 摘要 |
Enhanced rejection of out-of-vocabulary words, in which, based on applying an input gesture to hidden Markov models collectively modeling a vocabulary of training gestures, a likelihood that the input gesture matches each training gesture, and a quantity of states of the input gesture that match corresponding states of a modeled training gesture determined to have a highest likelihood are determined. The input gesture is rejected if the determined quantity does not satisfy a threshold. |
