跨语种语音情感识别研究是当前语音情感识别研究的热点和前沿课题。在跨语种语音情感识别研究中，由于从训练语音信号和从测试语音信号中提取的情感特征之间存在较大的差异，使得传统的基于训练数据得到的语音情感识别模型难以适用于测试样本数据。本项目旨在申请人原有语音情感识别与子空间学习研究基础上，深入开展跨语种语音情感识别研究，提出基于子空间迁移学习的跨语种语音情感识别理论与算法。所开展的研究主要包括以下内容（1）情感语音信号的特征提取与特征选择方法研究；（2）基于直推式迁移学习的判别子空间分析方法及其优化算法研究；（3）基于直推式迁移学习的核判别子空间分析方法及其优化算法研究；（4）基于子空间迁移学习的跨语种语音情感识别方法与算法研究。本项目侧重于子空间迁移学习方法统一模型构建及其在跨语种语音情感识别方面的运用，所提出的理论和算法思想可用于其他模态的情感识别研究中。

Cross-lingual speech emotion recognition is a hot and cutting-edge research topic in speech emotion recognition field. In the research of cross-lingual speech emotion recognition, the speech emotion recognition model trained based on the training speech utterances would not be well suitable for recognizing the emotion categories of the testing speech utterances because of the significant differences between the emotional speech features extracted from the training speech utterances and the testing utterances. In this project, we will investigate the cross-lingual speech emotion recognition issue based on our preliminary research work of speech emotion recognition and propose a novel subspace transfer learning approach to deal with the cross-lingual speech emotion recognition problem. The research contents of this project consist of the following four major parts: (1) the method of emotional speech feature extraction and speech feature selection; (2) the method of transductive transfer learning based linear discriminative subspace learning as well as the corresponding optimization approach; (3) the method of transductive transfer learning based kernel discriminative subspace learning method as well as the corresponding optimization approach; (4) the method and algorithm of cross-lingual speech emotion recognition based on subspace transfer learning. This project will focus more on the unified model building of the subspace transfer learning method and its application to the cross-lingual speech emotion recognition. The proposed methods may also be applicable to the emotion recognition problem of other modalities.