A recognition method includes extracting target data corresponding to a current window and padding data subsequent to the target data from sequence data; acquiring a state parameter corresponding to a previous window; and calculating a recognition result for the current window based on the state parameter, the extracted target data, and the extracted padding data using a recurrent model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A recognition method comprising: extracting target data corresponding to a current window and padding data subsequent to the target data from sequence data; acquiring a state parameter corresponding to a previous window; and calculating a recognition result for the current window based on the state parameter, the extracted target data, and the extracted padding data using a recurrent model. 2. The recognition method of claim 1, further comprising temporarily storing a state parameter corresponding to a last frame of the current window for use in a subsequent window. 3. The recognition method of claim 1, further comprising excluding a state parameter associated with a padding and output data of the padding data calculated based on the recurrent model. 4. The recognition method of claim 1, wherein the calculating of the recognition result comprises: unfolding the recurrent model by a window length and a padding length of the current window; and inputting the state parameter, the extracted target data, and the extracted padding data to the unfolded recurrent model for each frame and calculating output data corresponding to frames included in the current window. 5. The recognition method of claim 1, wherein the calculating of the recognition result comprises updating an input value of a node corresponding to a first frame of the current window based on a state parameter corresponding to a last frame of the previous window. 6. The recognition method of claim 1, wherein the extracting of the target data and the padding data comprises extracting data corresponding to a window length, starting from frame data subsequent to last frame data of the previous window, from the sequence data as the target data corresponding to the current window. 7. The recognition method of claim 1, wherein the extracting of the target data and the padding data comprises extracting data corresponding to a padding length, starting from frame data subsequent to last frame data of the current window, from the sequence data as the padding data corresponding to a padding. 8. The recognition method of claim 1, wherein the recurrent model is a bidirectional recurrent neural network that is trained to output a training output in response to a training input based on training data. 9. The recognition method of claim 1, wherein the recurrent model comprises a node corresponding to each frame included in a window and a padding; and the calculating of the recognition result comprises: updating a state parameter based on a forward pass of the node sequentially from a first frame to a last frame of the window; updating a state parameter based on a backward pass of the node sequentially from the last frame to the first frame of the window; and providing the state parameter based on the forward pass and the state parameter based on the backward pass to a node of a subsequent layer. 10. The recognition method of claim 1, wherein the sequence data corresponds to a voice signal; and the calculating of the recognition result comprises identifying a pronunciation from the target data corresponding to the current window. 11. The recognition method of claim 1, wherein the acquiring of the state parameter corresponding to the previous window comprises determining the state parameter corresponding to the previous window to be a default value in response to the current window being a first window with respect to the sequence data. 12. The recognition method of claim 1, wherein the extracting of the target data and the padding data comprises excluding the padding data in response to the current window being a last window with respect to the sequence data. 13. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform the recognition method of claim 1. 14. A recognition apparatus comprising: a storage configured to store a recurrent model; and a processor configured to: extract target data corresponding to a current window and padding data subsequent to the target data from sequence data, acquire a state parameter corresponding to a previous window, and calculate a recognition result for the current window based on the state parameter, the extracted target data, and the extracted padding data using the recurrent model. 15. A training method comprising: acquiring a recurrent model; extracting target data corresponding to a current window and padding data subsequent to the target data from a training input; acquiring a state parameter corresponding to a previous window; and training the recurrent model so that a training output mapped to the training input is calculated based on the state parameter, the extracted target data, and the extracted padding data. 16. The training method of claim 15, further comprises temporarily storing a state parameter corresponding to a last frame of the current window for use in a training based on training data of a subsequent window in response to data corresponding to the subsequent window being present in the training input. 17. The training method of claim 15, further comprising excluding output data associated with a padding calculated based on a state parameter associated with the padding using the recurrent model. 18. The training method of claim 15, wherein the training comprises updating a state parameter of a node corresponding to a first frame of the current window based on a state parameter corresponding to a last frame of the previous window. 19. The training method of claim 15, wherein the acquiring of the state parameter corresponding to the previous window comprises determining the state parameter corresponding to the previous window to be a default value in response to the current window being a first window with respect to the training input. 20. The training method of claim 15, wherein the extracting of the target data and the padding data comprises excluding the padding data in response to the current window being a last window with respect to the training input. 21. A recognition method comprising: extracting target data corresponding to a current window from sequence data; acquiring past context data corresponding to data preceding the current window in the sequence data; acquiring future context data corresponding to data following the current window in the sequence data, the past context data and the future context data being different types of data; and calculating a recognition result for the current window based on the past context data, the extracted target data, and the future context data using a recurrent model. 22. The recognition method of claim 21, wherein the acquiring of the past context data comprises acquiring, as the past context data, a state parameter corresponding to a previous window preceding the current window in the sequence data; and the acquiring of the future context data comprises extracting, as the future context data, padding data following the extracted target data from the sequence data. 23. The recognition method of claim 22, wherein the acquiring of the state parameter comprises: setting the state parameter to a default value in response to the current window being a first window of the sequence data; and acquiring, as the state parameter, a state parameter obtained during recognition performed on the previous window in response to the current window not being the first window of the sequence data. 24. The recognition method of claim 23, wherein the state parameter obtained during the recognition performed on the previous window is stored during the recognition performed on the previous window; and the acquiring of the state parameter obtained during the recognition performed on the previous window comprises retrieving the stored state parameter. 25. The recognition method of claim 22, wherein the previous window and the current window each comprise a plurality of frames; and the acquiring of the state parameter comprises acquiring a state parameter corresponding to a final frame of the previous window. 26. A training method comprising: acquiring a recurrent model; extracting target data corresponding to a current window from a training input; acquiring past context data corresponding to data preceding the current window in the training input; acquiring future context data corresponding to data following the current window in the training input, the past context data and the future context data being different types of data; and training the recurrent model so that a training output mapped to the training input is calculated based on the past context data, the extracted target data, and the future context data. 27. The training method of claim 26, wherein the acquiring of the past context data comprises acquiring, as the past context data, a state parameter corresponding to a previous window preceding the current window in the training input; and the acquiring of the future context data comprises extracting, as the future context data, padding data following the extracted target data from the training input. 28. The training method of claim 27, wherein the acquiring of the state parameter comprises: setting the state parameter to a default value in response to the current window being a first window of the training input; and acquiring, as the state parameter, a state parameter obtained during training performed on the previous window in response to the current window not being the first window of the training input. 29. The training method of claim 28, wherein the state parameter obtained during the training performed on the previous window is stored during the training performed on the previous window; and the acquiring of the state parameter obtained during the training performed on the previous window comprises retrieving the stored state parameter. 30. The training method of claim 27, wherein the previous window and the current window each comprise a plurality of frames; and the acquiring of the state parameter comprises acquiring a state parameter corresponding to a final frame of the previous window.