Improved Cross-Corpus Speech Emotion Recognition Using Deep Local Domain Adaptation
作者机构：College of Internet of Things Nanjing University of Posts and Telecommunications College of Computer and Software Nanjing Vocational University of Industry Technology College of Computer Nanjing University of Posts and Telecommunications Jiangsu High Technology Research Key Laboratory for Wireless Sensor Network
出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))
年 卷 期：2023年第32卷第3期
页 面：640-646页
核心收录：
学科分类：0711[理学-系统科学] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the National Natural Science Foundation of China (61572260) Postgraduate Research&Practice Innovation Program of Jiangsu Province (46035CX17789) Research Project of Nanjing Vocational University of Industry Technology (YK20-05-08)
主 题：Emotion recognition Adaptation models Transfer learning Speech recognition Complexity theory
摘 要：Due to the scarcity of high-quality labeled speech emotion data, it is natural to apply transfer learning to emotion recognition. However, transfer learning-based speech emotion recognition becomes more challenging because of the complexity and ambiguity of emotion. Domain adaptation based on maximum mean discrepancy considers marginal alignment of source domain and target domain, but not pay regard to class prior distribution in both domains, which results in the reduction of transfer efficiency. In order to address the problem, this study proposes a novel cross-corpus speech emotion recognition framework based on local domain adaption. A category-grained discrepancy is used to evaluate the distance between two relevant domains. According to research findings, the generalization ability of the model is enhanced by using the local adaptive *** with global adaptive and non-adaptive methods, the effectiveness of cross-corpus speech emotion recognition is significantly improved.
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