A Chinese natural speech complex emotion dataset based on emotion vector annotation method.
Although Chinese speech emotion recognition has received increasing attention, existing datasets still have defects such as insufficient naturalness, unreliable annotation, and single pronunciation style, which seriously hinder research progress. To address these issues, this paper proposes a Chines...
| Publicado en: | Language Resources & Evaluation Vol. 59; no. 3; pp. 3029 - 3051 |
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| Autores principales: | , , , , , , , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Sep2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=186909098&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 186909098 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Sep2025 vid: 59 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 186909098 10.1007/s10579-025-09845-0 ppf: 3029 ppct: 22 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.1MB tig: atl: A Chinese natural speech complex emotion dataset based on emotion vector annotation method. aug: au: Wu, Xiaolong Song, Chaobo Xiang, Shanshan Cao, Ronghe Feng, Chang Yilahun, Hankiz Xu, Mingxing Hamdulla, Askar Zheng, Thomas Fang affil: https://ror.org/059gw8r13 School of Computer Science and Technology, Xinjiang University, Urumqi, Xinjiang, China School of Electronical and Information Engineering, Jiangxi University of Engineering, Xinyu, China https://ror.org/03cve4549 Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China su: Emotion recognition Affective computing Artificial neural networks Emotional experience Chinese language sug: subj: Emotion recognition Affective computing Artificial neural networks Emotional experience Chinese language keyword: Complex emotions Dataset Emotion vector Psychology and Cognitive Sciences Psychology Speech emotion recognition ab: Although Chinese speech emotion recognition has received increasing attention, existing datasets still have defects such as insufficient naturalness, unreliable annotation, and single pronunciation style, which seriously hinder research progress. To address these issues, this paper proposes a Chinese natural speech complex emotion dataset (CNSCED) to provide natural data resources for Chinese speech affective computing. CNSCED was curated from publicly available Chinese news and interview television programs, capturing authentic emotional expressions encountered in daily life. The dataset comprises 14 h of speech from 454 speakers of diverse ages, totaling 15,777 samples. Acknowledging the inherent complexity and ambiguity of natural emotions, we propose an emotion vector annotation method. This method utilizes a vector composed of six meta-emotion dimensions (anger, sadness, aroused, happiness, surprise, and fear) of different intensities to describe any single or complex emotional state. CNSCED released two subtasks: complex emotion classification and complex emotion intensity detection. In the experiment, we evaluated the CNSCED using deep neural network models and provided a baseline result. To the best of our knowledge, CNSCED is the first publicly available Chinese natural speech complex emotion dataset, which can be freely downloaded from https://github.com/wuxlxju/CNSCED. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2025. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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