基于改进的排序式选举算法的语音情感融合识别

根据情感的连续空间模型，提出一种改进的排序式选举算法，实现多个情感分类器的融合，取得了很好的情感识别效果。首先以隐马尔可夫模型（HMM）和人工神经网络（ANN）为基础，设计了三种分类器；然后用改进的排序式选举算法，实现对三种分类器的融合。分别利用普通话情感语音库和德语情感语音库进行实验，结果表明，与几种传统融合算法相比，改进的排序式选举法能够取得更好的融合效果，其识别性能明显优于单分类器。该算法不仅简单，而且可移植性好，可用于其他任意多个情感分类器的融合。

Classifier fusion for speech emotion recognition based on improved queuing voting algorithm

According to the continuous space model for emotion, an improved queuing voting algorithm was proposed to implement the fusion of multiple emotion classifiers for a good emotion recognition result. Based on hidden Markov model (HMM) and artificial neural network (ANN), three kinds of classifier were designed. Then, the improved queuing voting algorithm was used to fuse them. Experimental study had been carried out by using Mandarin emotional speech database recoded and emotional speech database respectively. The results prove that the improved queuing voting algorithm can attain better fusion effect than conventional fusion algorithm and excel any single classifier evidently. The provided algorithm is not only easy to implement, but also transplantable. It is suitable for the fusion of any emotion classifiers.