TY - JOUR ID - 2063 TI - Multi-Task Feature Selection for Speech Emotion Recognition: Common Speaker-Independent Features Among Emotions JO - Journal of AI and Data Mining JA - JADM LA - en SN - 2322-5211 AU - Kalhor, E. AU - Bakhtiari, B. AD - Faculty of Computer Engineering and IT, Sadjad University of Technology, Mashhad, Iran. Y1 - 2021 PY - 2021 VL - 9 IS - 3 SP - 269 EP - 282 KW - Speech emotion recognition KW - Multi-Task Feature Selection KW - Speaker Independent Features KW - Cross-Corpus Feature Selection KW - Affective Processing DO - 10.22044/jadm.2021.9800.2118 N2 - Feature selection is the one of the most important steps in designing speech emotion recognition systems. Because there is uncertainty as to which speech feature is related to which emotion, many features must be taken into account and, for this purpose, identifying the most discriminative features is necessary. In the interest of selecting appropriate emotion-related speech features, the current paper focuses on a multi-task approach. For this reason, the study considers each speaker as a task and proposes a multi-task objective function to select features. As a result, the proposed method chooses one set of speaker-independent features of which the selected features are discriminative in all emotion classes. Correspondingly, multi-class classifiers are utilized directly or binary classifications simply perform multi-class classifications. In addition, the present work employs two well-known datasets, the Berlin and Enterface. The experiments also applied the openSmile toolkit to extract more than 6500 features. After feature selection phase, the results illustrated that the proposed method selects the features which is common in the different runs. Also, the runtime of proposed method is the lowest in comparison to other methods. Finally, 7 classifiers are employed and the best achieved performance is 73.76% for the Berlin dataset and 72.17% for the Enterface dataset, in the faced of a new speaker .These experimental results then show that the proposed method is superior to existing state-of-the-art methods. UR - https://jad.shahroodut.ac.ir/article_2063.html L1 - https://jad.shahroodut.ac.ir/article_2063_e9fd9e160048291a0c98ecb9dfd38358.pdf ER -