4. A learning method of a neural network according to claim 3, 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 first vectors representing the characteristics of the definite meaning.
3. A learning method of a neural network comprising: inputting first vector rows representing data to a data input layer; inputting second 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, the definite meaning is phoneme data representing a speech element and the plurality of first vector rows represent characteristics of the definite meaning analyzed in a time series; and performing learning for the data by having a plurality of first vector rows represent the definite meaning.