基于级联投影高斯混合模型的语音与心电情绪识别（英文）

Cascaded projection of Gaussian mixture model for emotion recognition in speech and ECG signals

提出了一种基于级联投影的高斯混合模型算法.首先,针对不同的特征维度计算高斯混合模型的边缘概率,依据边缘概率模型构造出多个子分类器,每个子分类器包含不同的特征组合.采用级联结构的框架对子分类器进行动态融合,从而获得对样本的自适应能力.其次,在心电情感信号和语音情感信号上验证了算法的有效性,通过实验诱发手段,采集了烦躁、喜悦、悲伤等情感数据.最后,探讨了情感特征参数(心率变异性、心电混沌特征,语句级静态特征等)的提取方法.研究了情感特征的降维方法,包括主分量分析、顺序特征选择、Fisher区分度和最大信息系数等方法.实验结果显示,所提算法能够在2种不同的场景中有效地提高情感识别的准确率.

A cascaded projection of the Gaussian mixture model algorithm is proposed.First,the marginal distribution of the Gaussian mixture model is computed for different feature dimensions, and a number of sub-classifiers are generated using the marginal distribution model.Each sub-classifier is based on different feature sets.The cascaded structure is adopted to fuse the sub-classifiers dynamically to achieve sample adaptation ability.Secondly,the effectiveness of the proposed algorithm is verified on electrocardiogram emotional signal and speech emotional signal.Emotional data including fidgetiness,happiness and sadness is collected by induction experiments.Finally,the emotion feature extraction method is discussed,including heart rate variability, the chaotic electrocardiogram feature and utterance level static feature.The emotional feature reduction methods are studied, including principle component analysis,sequential forward selection, the Fisher discriminant ratio and maximal information coefficient.The experimental results show that the proposed classification algorithm can effectively improve recognition accuracy in two different scenarios.