改进MFCC特征和MLA模型的语音情感识别OA

MFCC and its first-order differential features represent the static and dynamic information of speech,often used as emotional features in SER.In the traditional MFCC feature extraction process,balancing the speech signal-to-noise ratio through manual parameter tuning can easily lead to overcompensation.This article proposes two improvement methods to obtain EMFCC and AMFCC features,respectively.In order to achieve the best classification accuracy,an MLA model was constructed based on pooling layer,LSTM,and attention mechanism,which can effectively capture emotional information in features.A mixed feature consisting of MFCC and its first-order differential features,as well as two improved MFCC features,achieved an unweighted accuracy of 81.79%on the CASIA corpus.The results of the ablation experiment indicate that compared with other advanced recognition methods in the SER field,the improved MFCC feature has better performance advantages.