[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82862-en":3,"doc-seo-82862-105":29,"detail-sidebar-cat-0-en-105":91},{"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,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":20,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82862,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Ranking the Impact of Contextual Specialization in Neural Speech Enhancement","We systematically investigate neural speech enhancement systems, ranging from very small (~10 k parameters) to medium-large (~2–5 M parameters), which specialize to acoustic conditions using contextual information such as speaker identity, noise type, speaker gender, spoken language, and SNR. Fine-tuning generalist models on specific data subsets shows that specializing to a speaker’s identity consistently provides the largest gains in estimated speech intelligibility and quality. Cross-lingual tests further show language-specialized models can outperform multilingual generalists, enabling small adaptive models for edge devices.","RANKING THE IMPACT OF CONTEXTUAL SPECIALIZATION IN NEURAL SPEECH  \nENHANCEMENT  \nPeter Leer ⋆,†, Svend Feldt ⋆, Zheng-Hua Tan †, Jan Østergaard †, Jesper Jensen ⋆,†  \n⋆ Eriksholm Research Centre, Snekkersten, Denmark †Aalborg University, Department of Electronic Systems, Aalborg, Denmark  \narXiv :2607 .04826v1 [ ee ss .AS] 6 Jul 2026  \nABSTRACT  \nWe systematically investigate neural speech enhancement systems, ranging from very small (∼ 10 k parameters) to medium-large (∼2- 5 M parameters), which specialize to acoustic conditions using contextual information such as speaker identity, noise type, speaker gender, spoken language, and SNR. By fine-tuning generalist models on specific data subsets, we find that specializing to a speaker’s identity consistently yields the largest gains in estimated speech intelligibility and quality. In contrast, specializing to SNR, noise type, or gender offers only marginal benefits. Crucially, we show that a small model specialized to both a specific speaker and a specific noise type can match or exceed the performance of a generalist model ten times its size. Further, cross-lingual tests reveal that models specialized to a target language outperform multilingual generalists, suggesting that language is a salient feature for specialization. These findings highlight the potential of small, adaptive models for resourceconstrained applications like hearing aids, which specialize on-thefly to contextual information.  \nIndex Terms— Speech enhancement, personalization, contextual specialization  \n1. INTRODUCTION  \nUnderstanding speech in noisy situations is a common challenge, especially for people with impaired hearing [1] . To address this, edge devices, such as hearing aids, are often prescribed. However, despite substantial progress in hearing-aid technology and signal processing, enhancing speech intelligibility (SI) and speech quality (SQ) of noisy speech in real-world scenarios (e.g., crowded restaurants and public transport) remains a significant challenge [2] .  \nNeural network-based speech-enhancement models have demonstrated impressive improvements in both SI and SQ [3, 4] . Many high-performing models are trained on large, diverse datasets that allow the models to generalize across a wide range of speakers, noise types, and acoustic conditions [5] . While these models (referred to as generalists in the remainder of this paper) can provide robust generalization performance in unseen settings, the robustness often comes at a significant memory and computational cost. As a result, these generalist models are typically too resource-intensive for deployment on embedded platforms such as hearing aids.  \nIn many practical scenarios, an individual’s acoustic environment is often predictable and relatively stationary. A particular individual often interacts with a small set of familiar voices-such as family members, coworkers, or caregivers [6] -in recurring acoustic environments like their homes, workplaces, or their transit routes.  \nThis work is partly supported by Innovation Fund Denmark Case no. 4298-00010B  \nSimilarly, the types of background noise encountered in these environments-such as traffic, kitchen noise, competing speakers-are relatively few and can be characterized and used to estimate model parameters [7] . This stationary and predictable pattern suggests that models could benefit from adapting to these recurring conditions onthe-fly. If such adaptation enables small specialist models to deliver high performance in contextually familiar and stationary settings, it could dramatically reduce the resource requirements for effective speech enhancement (SE) on edge devices. These observations motivate a deeper investigation into the potential of smaller specialist SE systems over traditional generalist systems: (i) What can be gained by adapting SE systems to specific contextual information, and (ii) which types of contextual information offer the largest potential for performance gains","cbCaiuw2KmJ4YdHk","https://ap.wps.com/l/cbCaiuw2KmJ4YdHk","pdf",290599,5,1,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What contextual information most improves neural speech enhancement performance?\",\"answer\":\"Specializing to the speaker’s identity consistently yields the largest gains in estimated speech intelligibility and quality compared with specializing to SNR, noise type, or gender.\"},{\"question\":\"Can small specialized models match larger generalist models?\",\"answer\":\"Yes. A small model specialized to both a specific speaker and a specific noise type can match or exceed a generalist model ten times its size.\"},{\"question\":\"How do cross-lingual tests affect specialization choices?\",\"answer\":\"Models specialized to a target language outperform multilingual generalists, indicating that language is a salient feature for contextual specialization.\"}]",1784183533,13,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"ranking-the-impact-of-contextual-specialization-in-neural-speech-enhancement","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/ranking-the-impact-of-contextual-specialization-in-neural-speech-enhancement/82862/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What contextual information most improves neural speech enhancement performance?","Question",{"text":75,"@type":76},"Specializing to the speaker’s identity consistently yields the largest gains in estimated speech intelligibility and quality compared with specializing to SNR, noise type, or gender.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Can small specialized models match larger generalist models?",{"text":80,"@type":76},"Yes. A small model specialized to both a specific speaker and a specific noise type can match or exceed a generalist model ten times its size.",{"name":82,"@type":73,"acceptedAnswer":83},"How do cross-lingual tests affect specialization choices?",{"text":84,"@type":76},"Models specialized to a target language outperform multilingual generalists, indicating that language is a salient feature for contextual specialization.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":21,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":20,"slug":137},19,"General","general"]