Speech Emotion Recognition Method Based on Multiple Kernel Learning Feature Fusion
摘要
Extracting the Mel-Frequency Cepstral Coefficients(MFCC) in speech emotion recognition will lose the spectral feature information,resulting in a low accuracy of emotion recognition.Therefore,a speech emotion recognition method combining MFCC and spectrogram features is proposed.The MFCC features are extracted from the audio signal,the signal is converted into a spectral map,and the image features are extracted using a Convolutional Neural Network(CNN).On this basis,the Multiple Kernel Learning(MKL) algorithm is used to fuse the audio features,and the generated kernel functions are used to support the vector machine for emotion classification.Experimental results in two kinds of speech emotion data sets show that the speech emotion recognition accuracy of this method is as high as 96% compared with the classifier based on single feature.