  A speech emotion recognition algorithm based on multi-feature and
Multi-lingual fusion is proposed in order to resolve low recognition accuracy
caused by lack of large speech dataset and low robustness of acoustic features
in the recognition of speech emotion. First, handcrafted and deep automatic
features are extracted from existing data in Chinese and English speech
emotions. Then, the various features are fused respectively. Finally, the fused
features of different languages are fused again and trained in a classification
model. Distinguishing the fused features with the unfused ones, the results
manifest that the fused features significantly enhance the accuracy of speech
emotion recognition algorithm. The proposed solution is evaluated on the two
Chinese corpus and two English corpus, and is shown to provide more accurate
predictions compared to original solution. As a result of this study, the
multi-feature and Multi-lingual fusion algorithm can significantly improve the
speech emotion recognition accuracy when the dataset is small.
