Emotional recognition has universality and difference. Different language emotional databases have different emotional characteristics,and they also have similar emotional characteristics. This paper chooses IEMOCAP English emotion database,CASIA Chinese emotion database and EMO⁃BD German emotion database,and takes neutral,angry,happy and sad emotions as research objects to understand the situation of speech emotion recognition in single language corpus,mixed language corpus and cross⁃language corpus. Support Vector Machine (SVM),Convolutional Neural Network (CNN) and Long⁃Short Term Memory (LSTM) Network are used as classifiers to recognize emotions. The results show that there are similarities and cultural characteristics in speech emotion recognition patterns of different emotion corpora. It is found that English neutral emotion and Chinese sad emotion have good generalization of models,while English sad emotion and Chinese neutral emotion have better adaptability.

Keywords： cross⁃corpus ; speech emotion ; deep learning ; classifier ; transfer learning