一种新的结合情感数据场和蚁群策略的语音情感识别算法（英文）

A novel speech emotion recognition algorithm based on combination of emotion data field and ant colony search strategy

为了有效识别自发、非典型及未分割语音的情感以建立更自然的人机交互界面,提出了一种新的结合情感数据场和蚁群策略的语音情感识别算法.用情感数据场中势函数建立基于块的声学特征向量之间的内在联系.为识别自发语音情感,用人工蚁群模拟基于块的声学特征向量,然后用典型的蚁群策略研究每个人工蚂蚁在情感数据场的运动轨迹,并把该蚂蚁的运动轨迹作为对应的声学特征向量的情感标签.利用2012年连续音视频情感挑战赛中的语音数据对所提算法进行测试.实验结果表明:该算法较已有算法能更好地对基于块的语音情感进行识别.

In order to effectively conduct emotion recognition from spontaneous, non-prototypical and unsegmented speech so as to create a more natural human-machine interaction; a novel speech emotion recognition algorithm based on the combination of the emotional data field （EDF） and the ant colony search （ACS） strategy, called the EDF-ACS algorithm, is proposed. More specifically, the inter- relationship among the turn-based acoustic feature vectors of different labels are established by using the potential function in the EDF. To perform the spontaneous speech emotion recognition, the artificial colony is used to mimic the turn- based acoustic feature vectors. Then, the canonical ACS strategy is used to investigate the movement direction of each artificial ant in the EDF, which is regarded as the emotional label of the corresponding turn-based acoustic feature vector. The proposed EDF-ACS algorithm is evaluated on the continueous audio）＇visual emotion challenge （AVEC） 2012 dataset, which contains the spontaneous, non-prototypical and unsegmented speech emotion data. The experimental results show that the proposed EDF-ACS algorithm outperforms the existing state-of-the-art algorithm in turn-based speech emotion recognition.