The invention relates to a GAN-based speech confrontation sample generation method, which is characterized by comprising the steps of preprocessing an original speech data sample x, inputting the preprocessed original speech data sample x into a generator G to obtain an adversarial disturbance G(x), and using a formula (1) to construct an adversarial sample, the formula (1) being xadv = x + G(x),inputting the adversarial sample xadv into a discriminator D, and inputting the adversarial sample xadv into a target network f after the adversarial sample xadv passes through a Mel-frequency cepstrum coefficient MFCC feature extractor, calculating the loss lf of the target network, the adversarial loss lGAN of the discriminator, the hinge loss lhinge, the mean square error loss l2 and the loss lD of the discriminator, thereby obtaining a loss function l when the generator G is trained, S4, updating parameters of a generator and a discriminator through gradient back propagation of the loss function l obtained in the S4, obtaining an optimal generator through a formula (10), loading an original sample x into the optimal generator obtained in the S5 through the formula (10), and constructing to obtain a corresponding adversarial sample. Thus, the minimum disturbance can be effectively generated, and the speech quality can be ensured.