基于遗传优化的多级SVM语音情感识别

针对语音情感识别中特征维数高、识别率较低的问题,提出利用遗传算法进行特征降维,并构建二叉树结构的多级支持向量机(SVM)分类器进行语音多类情感识别的方案.首先对语音信号预处理后提取常用的情感特征,由于涉及特征较多,存在数据的冗余,采用遗传算法对提取的特征进行优化筛选;然后使用选出的最具情感区分能力的特征训练二叉树结构的多级SVM分类模型.在包含7种情感的柏林情感语料库上进行实验,结果证明提出的语音情感识别方案的有效性.

Multi layer SVM speech emotion recognition based on genetic optimization

Aiming at the problem of high feature dimension and low recognition rate in speech emotion recognition,In this paper,we propose a genetic algorithm for feature dimension reduction and construct a multi layer SVM classifier based on binary tree structure for the recognition of speech emotion.First,the common emotional features are extracted after preprocessing the speech signal.As there are many features and redundant data,the genetic algorithm is used to optimize the extracted features.Then,the hierarchical SVM classification model of the binary tree structure is trained by using the most discriminative features.The experimental results demonstrate the effectiveness of the proposed speech emotion recognition scheme on the Berlin emotion corpus containing 7 emotions.