Multi-Distributed Speech Emotion Recognition Based on Mel Frequency Cepstogram and Parameter Transfer  

作　　者：LIN Long TAN Liang 

机构地区：[1]School of Computer Science,Sichuan Normal University,Chengdu 610101,China [2]Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100101,China

出　　处：《Chinese Journal of Electronics》2022年第1期155-167,共13页电子学报（英文版）

基　　金：supported by National Natural Science Foundation of China(61373162);Sichuan Science and Technology Support Project(2019YFG0183);Visual Computing and Virtual Reality Sichuan Provincial Key Laboratory Project(KJ201402)。

摘　　要：Speech emotion recognition(SER)is the use of speech signals to estimate the state of emotion.At present,machine learning is one of the main research methods of SER,the test and training data S of traditional machine learning all have the same distribution and feature space,but the data of speech is accessed from different environments and devices,with different distribution characteristics in real life.Thus,the traditional machine learning method is applied to the poor performance of SER.This paper proposes a multi-distributed SER method based on Mel frequency cepstogram(MFCC)and parameter transfer.The method is based on single-layer long short-term memory(LSTM),pre-trained inceptionv3 network and multi-distribution corpus.The speech pre-processed MFCC is taken as the input of single-layer LSTM,and input to the pre-trained inception-v3 network.The features are extracted through the pre-trained inception-v3 model.Then the features are sent to the newly defined the fully connected layer and classification layer,let the parameters of the fully connected layer be finetuned,finally get the classification result.The experiment proves that the method can effectively complete the classification of multi-distribution speech emotions and is more effective than the traditional machine learning framework of SER.

关 键 词：Speech emotion recognition(SER) Machine learning Mel frequency cepstogram(MFCC) Parameter transfer Long short-term memory(LSTM) Inception-v3 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TN912.34[自动化与计算机技术—控制科学与工程]