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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 11
3279-3282,3348

Cross-corpus speech emotion recognition based on deep autoencoder subdomain adaptation

Zhuang Zhihao1
Fu Hongliang1
Tao Huawei1
Yang Jing1
Xie Yue2
Zhao Li3
1. Key Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China
2. School of Information & Communication Engineering, Nanjing Institute of Technology, Nanjing 211167, China
3. School of Information Science & Engineering, Southeast University, Nanjing 210018, China
DOI: 10.19734/j.issn.1001-3695.2021.04.0149

Abstract

To solve the problem of data distribution difference among different corpora, this paper proposed a cross-corpus speech emotion recognition algorithm based on subdomain adaptive deep autoencoder. Firstly, it used two depth autoencoders to obtain representative low-dimensional emotional features of source domain and target domain, respectively. Then, it used an adaptive sub-domain module based on LMMMD to achieve the alignment of feature distribution between source domain and target domain in different low-dimensional emotional category spaces. Finally, it used the tagged source domain data to supervise the training of the model. In the cross-corpus recognition scheme with eNTERFACE library as the source domain and Berlin library as the target domain, the accuracy of the proposed algorithm 5.26%~19.73% higher than that of other algorithms. In the cross-corpus recognition scheme with Berlin library as the source domain and eNTERFACE library as the target domain, the accuracy of the proposed algorithm is 7.34%~8.18% higher than that of other algorithms. Therefore, the proposed method can effectively extract the common sentiment features of different corpora and improve the performance of cross-corpus speech sentiment recognition.

Foundation Support

国家自然科学基金资助项目(62001215,61601170)
河南省教育厅自然科学项目(21A120003)
河南省科技厅科技攻关项目(202102210340)
河南工业大学高层次人才启动项目(2018BS037)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.04.0149
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 11
Section: Algorithm Research & Explore
Pages: 3279-3282,3348
Serial Number: 1001-3695(2021)11-013-3279-04

Publish History

[2021-11-05] Printed Article

Cite This Article

庄志豪, 傅洪亮, 陶华伟, 等. 基于深度自编码器子域自适应的跨库语音情感识别 [J]. 计算机应用研究, 2021, 38 (11): 3279-3282,3348. (Zhuang Zhihao, Fu Hongliang, Tao Huawei, et al. Cross-corpus speech emotion recognition based on deep autoencoder subdomain adaptation [J]. Application Research of Computers, 2021, 38 (11): 3279-3282,3348. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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