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ID 118257
著者
任, 福継 University of Electronic Science and Technology of China 徳島大学 教育研究者総覧 KAKEN研究者をさがす
Liu, Zheng Tokushima University
キーワード
Affective computing
Speech emotion recognition
Corpus construction
Active learning
Imbalanced dataset
資料タイプ
学術雑誌論文
抄録
Speech emotion recognition has been developed rapidly in recent decades because of the appearance of machine learning. Nevertheless, lack of corpus remains a significant issue. For actual speech emotion corpus construction, many professional actors are required to perform voices with various emotions in specific scenes. In the process of data labelling, since the number of samples of different emotion categories is extremely imbalanced, it is difficult to efficiently label the samples. Hence, we proposed an integrated active learning sampling strategy and designed an efficient framework for constructing speech emotion corpora in order to address the problems presented above. Comparing experiments with other active learning algorithms on 13 datasets, our method was shown to improve sampling efficiency. In addition, it is able to select small category samples to be labelled with preference in imbalanced datasets. During the actual corpus construction experiments, our method can prioritize selecting small class emotion samples. As even when the amount of labelled data is less than 50%, the accuracy rate still can reach 90%. This greatly enhances the efficiency of constructing the speech emotion corpus and fills in the gaps.
掲載誌名
IEEE Transactions on Affective Computing
ISSN
19493045
出版者
IEEE
13
4
開始ページ
1929
終了ページ
1940
発行日
2022-08-08
権利情報
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
EDB ID
出版社版DOI
出版社版URL
フルテキストファイル
言語
eng
著者版フラグ
著者版
部局
理工学系