2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDMLSP-37.5
Paper Title Cross-Corpus Speech Emotion Recognition Using Joint Distribution Adaptive Regression
Authors Jiacheng Zhang, Lin Jiang, Yuan Zong, Wenming Zheng, Li Zhao, Southeast University, China
SessionMLSP-37: Pattern Recognition and Classification 2
LocationGather.Town
Session Time:Thursday, 10 June, 16:30 - 17:15
Presentation Time:Thursday, 10 June, 16:30 - 17:15
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-PRCL] Pattern recognition and classification
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In this paper, we focus on the research of cross-corpus speech emotion recognition (SER), in which the training and testing speech signals in cross-corpus SER belong to different speech corpus. Due to this fact, mismatched feature distributions may exist between the training and testing speech feature sets degrading the performance of most originally well-performing SER methods. To deal with cross-corpus SER, we propose a novel domain adaptation (DA) method called joint distribution adaptive regression (JDAR). The basic idea of JDAR is to learn a regression matrix by jointly considering the marginal and conditional probability distribution between the training and testing speech signals and hence their feature distribution difference can be alleviated in the subspace spanned by the learned regression matrix. To evaluate the proposed JDAR, we conduct extensive cross-corpus SER experiments on EmoDB, eNTERFACE, and CASIA speech databases. Experimental results show that the proposed JDAR achieves satisfactory performance and outperforms most of state-of-the-art subspace learning based DA methods.