12. A learning method of a neural network according to claim 11, 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.
11. A learning method of a neural network comprising: inputting output vector values of a first output layer corresponding to first vector rows of a feedback input layer, the feedback input layer connected with a hidden layer and having neuron devices whose number is equal to that of neuron devices of the first output layer; inputting second vector rows to a data input layer; inputting third vector rows as a first instructor signal to the first output layer; and inputting a definite meaning as a second instructor signal to a second output layer, 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, wherein learning is carried out for data having a plurality of the second vector rows representing the definite meaning.