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

Aiming at 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, then the MEL cepstral coefficient (MFCC) and the hilbert marginal spectrum of each IMF are extracted.In order to experiment with the characteristic performance proposed in this paper, five kinds of emotions of anger, happiness, fear, sadness and neutrality in two kinds of speech databases (EMODB, RAVDESS) were selected as experimental samples. After extracting features according to this method, the extreme learning machine (ELM) was used for classification. The EMODB and SAVEE data sets obtained 89.8% and 95.5% recognition rates in simulation.The results show that compared with emd-based speech emotion features, the features proposed in this paper have better recognition performance, and the practicability of the method is verified.