Domain adaptation algorithms are widely used for cross-corpus speech emotion recognition. However， many domain adaptation algorithms lose the discrimination of target domain samples while pursuing the minimization of domain discrepancy， resulting in their presence at the decision boundary of the model in a high-density form， which degrades the performance of the model. Based on the above problem， a Decision Boundary Optimized Domain Adaptation （DBODA） method based cross-corpus speech emotion recognition was proposed. Firstly， the features were processed by using convolutional neural networks. Then， the features were fed into the Maximum Nuclear-norm and Mean Discrepancy （MNMD） module to maximize the nuclear norm of the sentiment prediction probability matrix of the target domain while reducing the inter-domain discrepancy， thereby enhancing the discrimination of the target domain samples and optimize the decision boundary. In six sets of cross-corpus experiments set up on the basis of Berlin， eNTERFACE and CASIA speech databases， the average recognition accuracy of the proposed method is 1.68 to 11.01 percentage points ahead of those of the other algorithms， indicating that the proposed model effectively reduces the sample density around the decision boundary and improves the prediction accuracy.