7. A learning method of a neural network comprising: inputting to a feedback input layer, output vector values of a hidden layer having a plurality of neuron devices, the plurality of neuron devices corresponding to first vector rows of the feedback input layer, the feedback input layer connected with the hidden layer and having a number of neuron devices equal to a number of neuron devices of the hidden layer; inputting second vector rows representing data to a data input layer; inputting third vector rows as a first instructor signal to a first output layer, and inputting a definite meaning as a second instructor signal to a second output layer; and performing learning for the data by having a plurality of second vector rows represent the definite meaning, wherein the definite meaning is phoneme data representing a speech element and the plurality of second vector rows represent characteristics of the definite meaning analyzed in a time series.
7. A learning method of a neural network comprising: inputting to a feedback input layer, output vector values of a hidden layer having a plurality of neuron devices, the plurality of neuron devices corresponding to first vector rows of the feedback input layer, the feedback input layer connected with the hidden layer and having a number of neuron devices equal to a number of neuron devices of the hidden layer; inputting second vector rows representing data to a data input layer; inputting third vector rows as a first instructor signal to a first output layer, and inputting a definite meaning as a second instructor signal to a second output layer; and performing learning for the data by having a plurality of second vector rows represent the definite meaning, wherein the definite meaning is phoneme data representing a speech element and the plurality of second vector rows represent characteristics of the definite meaning analyzed in a time series. 8. A learning method of a neural network according to claim 7, wherein one of spectral data, cepstrum data of the speech, and an output value data of the hidden layer of an auto-associative neural network is used as the plurality of second vector rows representing the characteristics of the definite meaning.