基于变分模态分解的语音情感识别方法

Speech emotion recognition based on variational mode decomposition

针对传统语音情感特征参数在进行情感分类时性能不佳的问题,该文提出了一种基于变分模态分解的语音情感识别方法。情感语音信号首先由变分模态分解提取固有模态函数,然后对所选主导固有模态函数进行重新聚合,再提取梅尔倒谱系数和各固有模态函数的希尔伯特边际谱。为了验证该文提出的特征性能,选用两种语音数据库(EMODB、RAVDESS)进行实验,按该文方法提取特征后使用极限学习机进行语音情感分类识别。实验结果表明:相比基于经验模态分解和集合经验模态分解的语音情感特征,该文提出的特征有更好的识别性能,验证了该方法的实用性。

In view of the problem of poor performance of traditional speech emotion feature parameters in emotion classification,this paper proposes a speech emotion recognition method based on variational mode decomposition(VMD).The emotion speech signal is first extracted by the VMD into the intrinsic mode functions(IMF),then the selected dominant IMFs are re-aggregated,after that the Mel frequency cepstral coefficents(MFCC)and the Hilbert marginal spectrum of each IMF are extracted.In order to verify the performance of the features proposed in this paper,two speech databases(EMODB、RAVDESS)are selected for the experiment.After extracting features according to the method of this paper,the extreme learning machine(ELM)is used for speech emotion classification and recognition.The experimental results show that compared with the emotion features based on empirical mode decomposition(EMD)and ensemble empirical mode decomposition(EEMD),the features proposed in this paper have better recognition performance,and the practicability of the method is verified.