Cross-Corpus Speech Emotion Recognition Based on Domain-Adaptive Least-Squares Regression

• Published in: IEEE signal processing letters Vol. 23; no. 5; pp. 585 - 589
• Main Authors: Zong, Yuan, Zheng, Wenming, Zhang, Tong, Huang, Xiaohua
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cross-corpus speech emotion recognition; Datasets; Domain adaptation; Feature extraction; Gold; Optimization; Speech; Speech processing; Testing; Training; Transfer learning; Cross-corpus speech emotion recognition (SER); domain adaptation; transfer learning
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2016.2537926

In this letter, a novel cross-corpus speech emotion recognition (SER) method using domain-adaptive least-squares regression (DaLSR) model is proposed. In this method, an additional unlabeled data set from target speech corpus is used to serve as an auxiliary data set and combined with the labeled training data set from source speech corpus for jointly training the DaLSR model. In contrast to the traditional least-squares regression (LSR) method, the major novelty of DaLSR is that it is able to handle the mismatch problem between source and target speech corpora. Hence, the proposed DaLSR method is very suitable for coping with cross-corpus SER problem. For evaluating the performance of the proposed method in dealing with the cross-corpus SER problem, we conduct extensive experiments on three emotional speech corpora and compare the results with several state-of-the-art transfer learning methods that are widely used for cross-corpus SER problem. The experimental results show that the proposed method achieves better recognition accuracies than the state-of-the-art methods.