[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-31754":3,"doc-seo-31754":27},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"file_id":15,"file_url":16,"file_type":17,"file_size":18,"view_count":4,"is_deleted":4,"is_public":19,"is_downloadable":19,"audit_status":19,"page_count":20,"language":21,"language_code":22,"table_of_contents":23,"faqs":24,"seo_title":13,"seo_description":14,"update_tm":25,"read_time":26},31754,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","GAN AraEmo Generative Data Augmentation for Low-Resource Arabic Speech Emotion Recognition","Arabic speech emotion recognition struggles with limited annotated datasets and high linguistic complexity across Arabic dialects. GAN-AraEmo proposes a generative adversarial network framework tailored to augment low-resource Arabic emotional speech data using a multi-scale attention-based generator and a spectrally normalized discriminator. An emotional consistency loss preserves emotional authenticity while generating diverse utterances across dialects. Experiments on three datasets yield 91.2% recognition accuracy versus a 67.5% baseline, with synthetic samples rated 4.2/5.0 for perceptual quality and 94.3% emotional authenticity, improving cross-dialect robustness.","cbCaijo2BXEXj8bv","https://ap.wps.com/l/cbCaijo2BXEXj8bv","pdf",3197965,1,29,"English","en","# Abstract\n# Introduction\n## Background and challenges in Arabic SER\n## Motivation for GAN-based augmentation and attention","[{\"question\":\"What problem does GAN-AraEmo address in Arabic speech emotion recognition?\",\"answer\":\"It targets the lack of annotated emotional speech data and the linguistic complexity introduced by diverse Arabic dialects, which reduces model robustness and generalization.\"},{\"question\":\"How is GAN-AraEmo designed to generate high-quality synthetic emotional speech?\",\"answer\":\"It uses a multi-scale generator with attention mechanisms and an enhanced discriminator with spectral normalization, together with an emotional consistency loss to maintain emotional authenticity.\"},{\"question\":\"What performance improvements and evaluation outcomes are reported for GAN-AraEmo?\",\"answer\":\"On three Arabic emotional speech datasets it reaches 91.2% recognition accuracy versus 67.5% for a baseline, with synthetic samples achieving 4.2/5.0 mean opinion score and 94.3% emotional authenticity in human evaluation, plus stronger cross-dialect generalization.\"}]",1780088489,73,{"code":4,"msg":28,"data":29},"ok",{"site_id":30,"language":22,"slug":31,"title":13,"keywords":32,"description":14,"schema_data":33,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":25},105,"gan-araemo-generative-data-augmentation-for-low-resource-arabic-speech-emotion-recognition","",{"@graph":34,"@context":83},[35,52,66],{"@type":36,"itemListElement":37},"BreadcrumbList",[38,42,46,49],{"item":39,"name":40,"@type":41,"position":19},"https://docshare.wps.com","Home","ListItem",{"item":43,"name":44,"@type":41,"position":45},"https://docshare.wps.com/document/","Document",2,{"item":47,"name":12,"@type":41,"position":48},"https://docshare.wps.com/document/research-report/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/gan-araemo-generative-data-augmentation-for-low-resource-arabic-speech-emotion-recognition/31754/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":39,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-05-29",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"What problem does GAN-AraEmo address in Arabic speech emotion recognition?","Question",{"text":73,"@type":74},"It targets the lack of annotated emotional speech data and the linguistic complexity introduced by diverse Arabic dialects, which reduces model robustness and generalization.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"How is GAN-AraEmo designed to generate high-quality synthetic emotional speech?",{"text":78,"@type":74},"It uses a multi-scale generator with attention mechanisms and an enhanced discriminator with spectral normalization, together with an emotional consistency loss to maintain emotional authenticity.",{"name":80,"@type":71,"acceptedAnswer":81},"What performance improvements and evaluation outcomes are reported for GAN-AraEmo?",{"text":82,"@type":74},"On three Arabic emotional speech datasets it reaches 91.2% recognition accuracy versus 67.5% for a baseline, with synthetic samples achieving 4.2/5.0 mean opinion score and 94.3% emotional authenticity in human evaluation, plus stronger cross-dialect generalization.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":30}]