ShEMO: a large-scale validated database for Persian speech emotion detection.
This paper introduces a large-scale, validated database for Persian called Sharif Emotional Speech Database (ShEMO). The database includes 3000 semi-natural utterances, equivalent to 3 h and 25 min of speech data extracted from online radio plays. The ShEMO covers speech samples of 87 native-Persian...
| Publicado en: | Language Resources & Evaluation Vol. 53; no. 1; pp. 1 - 17 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Springer Nature
Mar2019
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| 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=135114241&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 135114241 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Mar2019 vid: 53 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 135114241 10.1007/s10579-018-9427-x ppf: 1 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P size: 514KB tig: atl: ShEMO: a large-scale validated database for Persian speech emotion detection. aug: au: Mohamad Nezami, Omid Jamshid Lou, Paria Karami, Mansoureh affil: Bijar Branch, Islamic Azad University, Bijar, Iran Sharif University of Technology, Tehran, Iran su: Databases Speech Emotions Data extraction Internet radio sug: subj: Databases Speech Emotions Data extraction Internet radio keyword: Benchmark Emotion detection Emotional speech Persian Speech database ab: This paper introduces a large-scale, validated database for Persian called Sharif Emotional Speech Database (ShEMO). The database includes 3000 semi-natural utterances, equivalent to 3 h and 25 min of speech data extracted from online radio plays. The ShEMO covers speech samples of 87 native-Persian speakers for five basic emotions including anger, fear, happiness, sadness and surprise, as well as neutral state. Twelve annotators label the underlying emotional state of utterances and majority voting is used to decide on the final labels. According to the kappa measure, the inter-annotator agreement is 64% which is interpreted as "substantial agreement". We also present benchmark results based on common classification methods in speech emotion detection task. According to the experiments, support vector machine achieves the best results for both gender-independent (58.2%) and gender-dependent models (female = 59.4%, male = 57.6%). The ShEMO will be available for academic purposes free of charge to provide a baseline for further research on Persian emotional speech. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2019. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2019 holdings: @attributes: islocal: N |
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