According to an embodiment, a pattern recognition apparatus includes an RNN layer, as a middle layer, that includes an input converting unit and an RNN processor. The input converting unit performs conversion, for each step, on an input vector and a recurrent input vector, and calculates and outputs a converted vector of which the number of dimensions is smaller than the sum of the numbers of dimensions of respective the input vector and the recurrent input vector. The input vector is formed of a feature vector output from an input layer or an output of the RNN processor included in a lower RNN layer. The recurrent input vector is formed of an output of a previous step of the RNN processor. The RNN processor calculates an RNN output vector from the converted vector calculated in the input converting unit and outputs the RNN output vector, for each step.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A pattern recognition apparatus including at least one RNN layer serving as a middle layer forming a recurrent neural network between an input layer and an output layer, the pattern recognition apparatus being configured to output, from the output layer, a pattern series corresponding to a feature vector series input to the input layer, wherein the RNN layer includes an input converting unit and an RNN processor, the input layer successively outputs feature vectors forming the feature vector series one at a time for each step, the input converting unit of the RNN layer performs conversion, for each step, on an input vector and a recurrent input vector, and calculates and outputs a converted vector of which the number of dimensions is smaller than the sum of the number of dimensions of the input vector and the number of dimensions of the recurrent input vector, the input vector being formed of the feature vector output from the input layer or an output of the RNN processor included in the lower RNN layer, the recurrent input vector being formed of an output of a previous step of the RNN processor included in the RNN layer, the RNN processor of the RNN layer calculates an RNN output vector from the converted vector calculated in the input converting unit included in the RNN layer and outputs the RNN output vector, for each step, and the output layer calculates the pattern series from the RNN output vector calculated in the RNN processor included in an uppermost layer of the RNN layer and outputs the pattern series. 2. The apparatus according to claim 1, wherein the input converting unit calculates the converted vector vt at a step t, from the input vector xt at the step t and the recurrent input vector mt−1, with vt=Wvx·xt+Wvm·mt−1 (Wvx is a weight matrix for the input vector xt; and Wvm is a weight matrix for the recurrent input vector mt−1), or vt=k(Wvx·xt·Wvm·mt−1+bv) (k(⋅) is a nonlinear function applied to each vector; Wvx is a weight matrix for the input vector xt, Wvm is a weight matrix for the recurrent input vector mt−1; bv is a bias vector having I dimensions where the number of dimensions of the converted vector vt is I). 3. The apparatus according to claim 2, wherein the RNN processor calculates the RNN output vector mt at the step t from the converted vector vt at the step t with mt=σ(Wmv·vt+bm) (Wmv is a weight matrix for the converted vector vt; bm is a bias vector; and σ(⋅) is a nonlinear function applied to each element of the vector). 4. The apparatus according to claim 3, wherein, when the number of RNN layers is 1, the input converting unit calculates and outputs the converted vector satisfying a condition of I < ( N + M ) · M N + 2  M with: N denoting the number of dimensions of the input vector; M denoting the number of dimensions of the recurrent input vector; and I denoting the number of dimensions of the converted vector. 5. The apparatus according to claim 3, wherein, when the number of RNN layers is R (R is 2 or a higher integer), the input converting unit calculates and outputs the converted vector satisfying a condition of ∑ r = 1 R  ( ( N r + 2 · M r ) · I r ) < ∑ r = 1 R  ( ( N r + M r ) · M r ) with: Nr denoting the number of dimensions of the input vector of an r-th layer (1≤r≤R) at the step t; Mr denoting the number of dimensions of the recurrent input vector; and Ir denoting the number of dimensions of the converted vector. 6. The apparatus according to claim 2, wherein the RNN processor is a long short-term memory (LSTM), and calculates the RNN output vector mt at the step t from the converted vector vt with it=σ(Wiv·vt+wic ct−1+bi) ft=σ(Wfv·vt+wfc ct−1+bf) ct=ft ct−1+it g(Wcv·vt+bc) ot=σ(Wov·vt+woc ct+bo) mt=ot h(ct) (it is an input gate activation vector at the step t; ft is a forget gate activation vector at the step t; ct is a memory cell activation vector at the step t; ct−1 is a memory cell activation vector at a step t−1; each of Wiv, Wfvr, Wcv, and Wov is a weight matrix for the converted vector vt; each of wic, wfc, and woc is a weight vector; each of bi, bf, bc, and bo is a bias vector; is element-wise multiplication; and each of σ(⋅), g(⋅), and h(⋅) is a nonlinear function applied to each element of the vector). 7. The apparatus according to claim 6, wherein, when the number of RNN layers is 1, the input converting unit calculates and outputs the converted vector satisfying a condition of I < 4 · ( N + M ) · M N + 5  M with: N denoting the number of dimensions of the input vector; M denoting the number of dimensions of the recurrent input vector; and I denoting the number of dimensions of the converted vector. 8. The apparatus according to claim 6, wherein, when the number of RNN layers is R (R is 2 or a higher integer), the input converting unit calculates and outputs the converted vector satisfying a condition of ∑ r = 1 R  ( ( N r + 5 · M r ) · I r ) < ∑ r = 1 R  ( 4 · ( N r + M r ) · M r ) with: Nr denoting the number of dimensions of the input vector of an r-th layer (1≤r≤R) at the step t; Mr denoting the number of dimensions of the recurrent input vector; and Ir denoting the number of dimensions of the converted vector. 9. The apparatus according to claim 1, wherein the input converting unit calculates the converted vector vt with vt=[vxtT, vctT, vmtT]T vxt=Wvxt·xt vct=Wvxc·xt+Wvmc·mt−1 vmt=Wvmi·mt (.T is a transpose of a matrix; each of Wvxi and Wvxc is a weight matrix for the input vector xt; and each of Wvmi and Wvmc is a weight matrix for the recurrent input vector mt−1), or vt=[vxtT, vctT, vmtT]T vxt=k(Wvxi·xt+bvx) vct=k(Wvxc·xt+Wvmc·mt−1+bvc) vmt=k(Wvmt·mt+bvm) (.T is a transpose of a matrix; k(⋅) is a nonlinear function applied to each vector; each of Wvxi and Wvxc is a weight matrix for the input vector xt; each of Wvmi and Wvmc is a weight matrix for the recurrent input vector mt−1; bvx is a bias vector having Ix dimensions where the number of dimensions of a component vxt of the converted vector is Ix; bvc is a bias vector having Ic dimensions where the number of dimensions of a component vct of the converted vector is Ic; and bvm is a bias vector having Im dimensions where the number of dimensions of a component vmt of the converted vector is Im) with vxt, vct, and vmt denoting a component calculated only from the input vector xt at a step t, a component calculated from the input vector xt and the recurrent input vector mt−1, and a component calculated only from the recurrent input vector mt−1, respectively, in the converted vector vt at the step t. 10. The apparatus according to claim 9, wherein the RNN processor calculates the RNN output vector mt at the step t from the converted vector vt at the step t with mt=σ(Wmv·vt+bm) (Wmv is a weight matrix for the converted vector vt; bm is a bias vector; and σ(⋅) is a nonlinear function applied to each element of the vector). 11. The apparatus according to claim 10, wherein, when the number of RNN layers is 1, the input converting unit calculates and outputs the converted vector that satisfies a condition of ∑ r = 1 R  ( ( N r + 2 · M r ) · I r ) < ∑ r = 1 R  ( ( N r + M r ) · M r + I rm · N r + I rx · M r ) with: N denoting the number of dimensions of the input vector; M denoting the number of dimensions of the recurrent input vector; I denoting the number of dimensions of the converted vector; and Ix and Im denoting the number of dimensions of the component calculated only from the input vector and the number of dimensions calculated only from the recurrent input vector, respectively, in the converted vector. 12. The apparatus according to claim 10, wherein, when the number of RNN layers is R (R is 2 or a higher integer), the input converting unit calculates and outputs the converted vector satisfying a condition of I < ( N + M ) · M + I m · N + I x · M N + 2 · M with: Nr denoting the number of dimensions of the input vector of an r-th layer (1≤r≤R) at the step t; Mr denoting the number of dimensions of the recurrent input vector; Ir denoting the number of dimensions of the converted vector; and Irx, Iro, and Irm denoting the number of dimensions of the component calculated only from the input vector, the number of dimensions of the component calculated from the input vector and the recurrent input vector, and the number of dimensions calculated only from the recurrent input vector, respectively, in the converted vector. 13. The apparatus according to claim 9, wherein the RNN processor is a long short-term memory (LSTM), and calculates the RNN output vector mt at the step t from the converted vector vt with it=σ(Wiv·vt+wic ct−1+bj) ft=σ(Wfv·vt+wfc ct−1+bf) ct=ft ct−1+it g(Wcv·vt+bc) ot=σ(Wov·vt+woc ct+bo) mt=ot h(ct) (it is an input gate activation vector at the step t; ft is a forget gate activation vector at the step t; ct is a memory cell activation vector at the step t; ct−1 is a memory cell activation vector at a step t−1; each of Wiv, WfV, Wcv, and Wov is a weight matrix for the converted vector vt; each of wic, wfc, and woc is a weight vector; each of bi, bf, bc, and bo is a bias vector; is element-wise multiplication; and each of σ(⋅), g(⋅), and h(⋅) is a nonlinear function applied to each element of the vector). 14. The apparatus according to claim 13, wherein, when the number of RNN layers is 1, the input converting unit calculates and outputs the converted vector satisfying a condition of I < 4 · ( N + M ) · M + I m · N + I x · M N + 5  M with: N denoting the number of dimensions of the input vector; M denoting the number of dimensions of the recurrent input vector; I denoting the number of dimensions of the converted vector; and Ix and Im denoting the number of dimensions of the component calculated only from the input vector and the number of dimensions calculated only from the recurrent input vector, respectively, in the converted vector. 15. The apparatus according to claim 13, wherein, when the number of RNN layers is R (R is 2 or a higher integer), the input converting unit calculates and outputs the converted vector satisfying a condition of ∑ r = 1 R  ( ( N r + 5 · M r ) · I r ) < ∑ r = 1 R  ( 4 · ( N r + M r ) · M r + I rm · N r + I rx · M r ) with: Nr denoting the number of dimensions of the input vector of an r-th layer (1≤r≤R) at the step t; Mr denoting the number of dimensions of the recurrent input vector; Ir denoting the number of dimensions of the converted vector; and Trx, Irc, and Irm denoting the number of dimensions of the component calculated only from the input vector, the number of dimensions of the component calculated from the input vector and the recurrent input vector, and the number of dimensions calculated only from the recurrent input vector, respectively, in the converted vector. 16. A pattern recognition method executed by a pattern recognition apparatus including at least one RNN layer serving as a middle layer forming a recurrent neural network between an input layer and an output layer, the pattern recognition apparatus being configured to output, from the output layer, a pattern series corresponding to a feature vector series input to the input layer, wherein the RNN layer includes an input converting unit and an RNN processor, the pattern recognition method comprising: successively outputting, by the input layer, feature vectors forming the feature vector series one at a time for each step, performing conversion, by the input converting unit of the RNN layer, for each step, on an input vector and a recurrent input vector, and calculating and outputting a converted vector of which the number of dimensions is smaller than the sum of the number of dimensions of the input vector and the number of dimensions of the recurrent input vector, the input vector being formed of the feature vector output from the input layer or an output of the RNN processor included in the lower RNN layer, the recurrent input vector being formed of an output of a previous step of the RNN processor included in the RNN layer, by the RNN processor of the RNN layer, calculating an RNN output vector from the converted vector calculated in the input converting unit included in the RNN layer and outputting the RNN output, for each step, and by the output layer, calculating the pattern series from the RNN output vector calculated in the RNN processor included in an uppermost layer of the RNN layer and outputting the pattern series. 17. A computer program product having a computer readable medium including programmed instructions, wherein the instructions, when executed by a computer, cause the computer to function as a pattern recognition apparatus including at least one RNN layer serving as a middle layer forming a recurrent neural network between an input layer and an output layer, the pattern recognition apparatus being configured to output, from the output layer, a pattern series corresponding to a feature vector series input to the input layer, the RNN layer including an input converting unit and an RNN processor, the instruction being configured to cause the computer to execute: successively outputting, by the input layer, feature vectors forming the feature vector series one at a time for each step, performing conversion, by the input converting unit of the RNN layer, for each step, on an input vector and a recurrent input vector, and calculating and outputting a converted vector of which the number of dimensions is smaller than the sum of the number of dimensions of the input vector and the number of dimensions of the recurrent input vector, the input vector being formed of the feature vector output from the input layer or an output of the RNN processor included in the lower RNN layer, the recurrent input vector being formed of an output of a previous step of the RNN processor included in the RNN layer, by the RNN processor of the RNN layer, calculating an RNN output vector from the converted vector calculated in the input converting unit included in the RNN layer, and outputting the RNN output vector, for each step, and by the output layer, calculating the pattern series from the RNN output vector calculated in the RNN processor included in an uppermost layer of the RNN layer, and outputting the pattern series.