A computer-implemented voice recognition method implemented using a bidirectional recurrent neural network, comprising: acquiring a voice signal as sequence data; segmenting the sequence data to obtain a plurality of non-overlapping windows (510, 520, 530, 590), each window including a plurality of frames, wherein the RNN includes at least one node corresponding to each frame; for a current window which is the first window: extracting target data corresponding to the current window as well as padding data, wherein the padding data is data subsequent to the last frame of the current window, setting a state parameter to a default value, calculating a recognition result for the current window based on the state parameter, the extracted target data, and the extracted padding data, storing a state parameter of a node corresponding to a last frame of the current window; for each subsequent window as a current window: extracting target data corresponding to the current window as well as padding data wherein the padding data is data subsequent to the last frame of the current window, acquiring the stored state parameter of the node corresponding to the last frame of the previous window (1020, 1214) instead of adding sequence data preceding the current window, calculating (1030, 1222) a recognition result for the current window based on the state parameter of the node corresponding to the last frame of the previous window, the extracted target data, and the extracted padding data, and storing a state parameter of a node corresponding to a last frame of the current window; wherein the calculating (1030, 1222) of the recognition result comprises identifying pronunciation information, including at least one phoneme, from the target data corresponding to the current window by: unfolding the bidirectional RNN 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 bidirectional RNN for each frame and calculating output data corresponding to frames included in the current window. acquiring a voice signal as sequence data; segmenting the sequence data to obtain a plurality of non-overlapping windows (510, 520, 530, 590), each window including a plurality of frames, wherein the RNN includes at least one node corresponding to each frame; for a current window which is the first window: extracting target data corresponding to the current window as well as padding data, wherein the padding data is data subsequent to the last frame of the current window, setting a state parameter to a default value, calculating a recognition result for the current window based on the state parameter, the extracted target data, and the extracted padding data, storing a state parameter of a node corresponding to a last frame of the current window; extracting target data corresponding to the current window as well as padding data, wherein the padding data is data subsequent to the last frame of the current window, setting a state parameter to a default value, calculating a recognition result for the current window based on the state parameter, the extracted target data, and the extracted padding data, storing a state parameter of a node corresponding to a last frame of the current window; for each subsequent window as a current window: extracting target data corresponding to the current window as well as padding data wherein the padding data is data subsequent to the last frame of the current window, acquiring the stored state parameter of the node corresponding to the last frame of the previous window (1020, 1214) instead of adding sequence data preceding the current window, calculating (1030, 1222) a recognition result for the current window based on the state parameter of the node corresponding to the last frame of the previous window, the extracted target data, and the extracted padding data, and storing a state parameter of a node corresponding to a last frame of the current window; extracting target data corresponding to the current window as well as padding data wherein the padding data is data subsequent to the last frame of the current window, acquiring the stored state parameter of the node corresponding to the last frame of the previous window (1020, 1214) instead of adding sequence data preceding the current window, calculating (1030, 1222) a recognition result for the current window based on the state parameter of the node corresponding to the last frame of the previous window, the extracted target data, and the extracted padding data, and storing a state parameter of a node corresponding to a last frame of the current window; wherein the calculating (1030, 1222) of the recognition result comprises identifying pronunciation information, including at least one phoneme, from the target data corresponding to the current window by: unfolding the bidirectional RNN 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 bidirectional RNN for each frame and calculating output data corresponding to frames included in the current window. unfolding the bidirectional RNN 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 bidirectional RNN for each frame and calculating output data corresponding to frames included in the current window.