[Object] An object is to provide a training method of improving training of a recurrent neural network (RNN) using time-sequential data. [Solution] The training method includes a step 220 of initializing the RNN, and a training step 226 of training the RNN by designating a certain vector as a start position and optimizing various parameters to minimize error function. The training step 226 includes: an updating step 250 of updating RNN parameters through Truncated BPTT using consecutive N (N≥3) vectors having a designated vector as a start point and using a reference value of a tail vector as a correct label; and a first repetition step 240 of repeating the process of executing the training step by newly designating a vector at a position satisfying a prescribed relation with the tail of N vectors used at the updating step until an end condition is satisfied. The vector at a position satisfying the prescribed relation is positioned at least two vectors behind the designated vector.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A recurrent neural network (RNN) training method performed by a computer using training data prepared in advance; wherein the training data includes a sequence of vectors each consisting of a feature of data to be trained and a reference value; the training method comprising the steps of: initializing the RNN; a first designating step of designating, as a training start position, a certain vector in the sequence of vectors; and a training step of training the RNN by optimizing various parameters of the RNN such that a prescribed error function calculated for the training data is minimized; wherein the training step includes an updating step of updating the parameters of the RNN by Truncated BPTT, using, of the sequence of vectors, consecutive N vectors, where N is an integer not smaller than 3, having the designated vector as a head, and having a reference value of a vector at the tail of the consecutive N vectors as a correct label, and a first repetition step of repeating, until a predetermined end condition is satisfied, a process of executing the training step by newly designating a vector at a position satisfying a prescribed relation with the vector at the tail of the N vectors used in the updating step; wherein the vector at the position satisfying the prescribed relation is a vector positioned at least two or more vectors behind the designated vector designated at the sciccting first designating step. 2. The RNN training method according to claim 1, wherein the vector at the position satisfying the prescribed relation is a vector positioned at the tail of the N vectors. 3. The RNN training method according to claim 1, further comprising: a second designating step, responsive to a determination that the end condition is satisfied at the first repetition step, of newly designating a vector, positioned by a number smaller than the N behind the vector designated at the first designating step, as a start position of next training; and a step of executing the training step until the end condition is satisfied. 4. The RNN training method according to claim 1, further comprising: a second repetition step, responsive to a determination that the end condition is satisfied at the first repetition step, of executing the training step repeatedly while designating a vector at a position moved further backward by one from the certain vector for each repetition as a start position of next training, until a process designating a vector positioned N−1 behind the certain vector is finished. 5. A computer program causing a computer to execute all the steps of RNN training according to claim 1. 6. A speech recognition device, comprising: framing means for framing an input speech signal with a prescribed shift length and a prescribed frame length; feature extracting means for extracting a prescribed speech feature from each frame of speech signal output from the framing means and outputting a feature vector; an acoustic model implemented by a RNN trained by the training method according to claim 1, receiving as an input a sequence of feature vectors output from the feature extracting means and outputting, every time a feature vector is input, a vector having as elements probability of a speech of the frame corresponding to the feature vector being each element of a set of assumed phonemes; and a speech recognition decoder outputting, by a probabilistic process using a sequence of vectors output from the acoustic model and a statistical language model, a word sequence having highest likelihood of generating the speech signal.