基于深度信念网络的语音情感识别

摘要

针对语音情感识别中的特征提取问题, 通过多层深度信念网络 (DBN) 自动提取语音信号中的情感特征, 把连续多帧的语音拼接在一起, 形成一个高维抽象特征, 将深度信念网络训练好的特征作为极限学习机 (ELM) 分类器的输入端, 最终建立一个语音情感识别系统.实验结果表明, 在CASIA情感语音数据库中, 本方法优于其他情感识别方法.%Aiming at the feature extraction problem in speech emotion recognition, the hierarchical deep belief network (DBN) was used to extract the emotion characteristics of speech signal automatically, and the continuous multi frame speech was combined together to form a highdimensional abstract feature.The feature trained by deep belief network was used as the input of the limit learning machine (ELM) classifier, and finally a speech emotion recognition system was established.The experimental results in CASIA emotional speech database show that this method is superior to other methods of emotion recognition.