DEEP LEARNING METHOD FOR SPEECH EMOTION RECOGNITION BASED ON IMPROVED K-MEAN CLUSTERING

Aimed at the problems of low accuracy and high time complexity in current speech emotion recognition (SRE) methods, a deep learning method for speech emotion recognition based on the improved k-mean clustering is proposed. The improved k-mean clustering algorithm was used to select the key segments which reflected the emotional features from the whole audio signal. The selected sequence was transformed into a spectrum by using short-time Fourier transform. The deep residual model ResNet and deep Bi-LSTM network were used to learn the hidden features related to emotion in the representation spectrum from space and time. The final sentiment classification was obtained based on Softmax classifier. Experimental results show that the proposed method has obvious advantages over other recognition methods, which improves the emotion recognition rate and reduces the processing time of the model.