Volume 31 Issue 1
Jan.  2022
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LIN Long, TAN Liang. Multi-Distributed Speech Emotion Recognition Based on Mel Frequency Cepstogram and Parameter Transfer[J]. Chinese Journal of Electronics, 2022, 31(1): 155-167. doi: 10.1049/cje.2020.00.080
Citation: LIN Long, TAN Liang. Multi-Distributed Speech Emotion Recognition Based on Mel Frequency Cepstogram and Parameter Transfer[J]. Chinese Journal of Electronics, 2022, 31(1): 155-167. doi: 10.1049/cje.2020.00.080

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

doi: 10.1049/cje.2020.00.080
Funds:  This work was supported by National Natural Science Foundation of China (61373162), Sichuan Science and Technology Support Project (2019YFG0183), and Visual Computing and Virtual Reality Sichuan Provincial Key Laboratory Project (KJ201402)
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  • Author Bio:

    was born in 1995. He received the B.S. degree in engineering from Sichuan Normal University, Chengdu, China, in 2018. He is currently pursuing an M.S. degree in software engineering at Sichuan Normal University, Chengdu, China. His main research interest is emotion recognition. He also worked on a digital teaching patent. (Email: 569074330@qq.com)

    (corresponding author) received the Ph.D. degree in computer science from University of Electronic Science and Technology of China in 2007. He is a Professor of School of Computer Science, Sichuan Normal University. His research interests include cloud computing, big data, blockchain, trusted computing and network security. (Email: jkxy_tl@sicnu.edu.cn)

  • Received Date: 2020-03-17
  • Accepted Date: 2020-06-03
  • Available Online: 2021-10-22
  • Publish Date: 2022-01-05
  • 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 dataS 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 inception-v3 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 fine-tuned, 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.
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