A method for training a voiceprint extraction model, the voiceprint extraction model being a neural network model, the method characterized by comprising: acquiring voice data of a user on T devices, T being a positive integer greater than or equal to 2; extracting acoustic features based on the voice data to construct a training data set; inputting the training data set into the neural network model; training the neural network model; acquiring a loss function of each training of the neural network model; and determining that the training of the neural network model is completed when the loss function converges, wherein the loss function is a weighted sum of a classification loss function, a reconstruction loss function, a difference loss function and a similarity loss function, wherein the difference loss function is for representing orthogonality between a shared space and a private space, wherein the shared space and the private space are respectively established for data from different channels, the shared space being shared by the different channels and configured to learn shared features which are voiceprint features, the private space being limited by each of the different channels and configured to learn private features which are channel noises generated by each of the T devices, wherein the orthogonality between the shared space and the private space represents independency between the shared space and the private space, wherein the classification loss function is for predicting a final output label, wherein the reconstruction loss function is for ensuring that the private features of the private space can act on learning objectives, and wherein the similarity loss function is for ensuring that a network can extract similarity features of different devices. acquiring voice data of a user on T devices, T being a positive integer greater than or equal to 2; extracting acoustic features based on the voice data to construct a training data set; inputting the training data set into the neural network model; training the neural network model; acquiring a loss function of each training of the neural network model; and determining that the training of the neural network model is completed when the loss function converges, wherein the loss function is a weighted sum of a classification loss function, a reconstruction loss function, a difference loss function and a similarity loss function, wherein the difference loss function is for representing orthogonality between a shared space and a private space, wherein the shared space and the private space are respectively established for data from different channels, the shared space being shared by the different channels and configured to learn shared features which are voiceprint features, the private space being limited by each of the different channels and configured to learn private features which are channel noises generated by each of the T devices, wherein the orthogonality between the shared space and the private space represents independency between the shared space and the private space, wherein the classification loss function is for predicting a final output label, wherein the reconstruction loss function is for ensuring that the private features of the private space can act on learning objectives, and wherein the similarity loss function is for ensuring that a network can extract similarity features of different devices. <EOS>