[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83702-en":3,"doc-seo-83702-105":30,"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":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83702,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Deriving Benchmarking Datasets from Long-Form Recordings: Challenges and Opportunities","Long-form recordings (LFRs) of child-centered audio offer ecologically valid material for studying early language development, yet three barriers restrict their use: cross-site heterogeneity in formats and consent structures, the lack of standardized benchmarks to evaluate cross-language generalization, and privacy risks because ML pipelines often ignore governance for sensitive child speech. The paper proposes a unified framework with (1) standardized collection of 27 datasets, (2) a replicable pipeline for four benchmarks, and (3) ELSI role-based ethical governance, validated via a voice type classification case study.","Deriving Benchmarking Datasets from Long-Form Recordings: Challenges and Opportunities  \nKaveri K. Sheth  1 ,∗∗, Lawrence Borst  1, Tarek Kunze  1, Marvin Lavechin  2, Okko Ra¨sa¨nen   \n3, Sho Tsuji  1, Loann Peurey  1, Alix Bourre´e 1 , Alejandrina Cristia  1  \n1 LAAC, LSCP, DEC, ENS, EHESS, CNRS, PSL University, Paris, France  \n2 Laboratoire d’Informatique et Systmes, Universit Aix-Marseille, CNRS, France  \n3 Signal Processing Research Centre, Tampere University, Finland  \n[ksheth@ens.psl.eu](ksheth@ens.psl.eu)  \narXiv :2607 .03201v1 [ ee ss .AS] 3 Jul 2026  \nAbstract  \nLong-form recordings (LFRs) of child-centered audio are ecologically valid sources for studying early language development, but three problems limit their use. First, LFR corpora are collected across sites with heterogeneous formats and consent structures, making cross-corpus use non-trivial. Second, without standardized benchmarks, assessing whether tools generalize across languages and conditions is hard. Third, ML workflows rarely respect privacy constraints governing sensitive child speech. This paper presents a framework addressing all three: a standardized collection of 27 child-centered datasets built with open-source tools (S1); a replicable pipeline for four speech-processing benchmarks (S2); and ELSI, a role-based ecosystem embedding ethical governance into the ML workflow (S3) . We demonstrate the framework via a voice type classification case study and show the three solutions are mutually dependent.  \nIndex Terms: dataset standardization, data ethics, open-source tools, naturalistic recordings, data curation  \n1. Introduction  \nLong-form recordings (LFRs) of child-centered audio, typically obtained via wearable microphones worn by young children throughout a full day, provide unmatched ecological validity for studying language input, production, and acquisition [1, 2, 3] . However, three problems have prevented LFR corpora from being fully leveraged for speech tool development and evaluation.  \nProblem 1: Cross-corpus heterogeneity makes joint use non-trivial. LFR corpora have been collected by independent research teams worldwide, each with its own annotation format, metadata conventions, directory structure, and participant consent framework. A team wishing to train a classifier on several corpora, or evaluate a model across different linguistic communities, must currently work around these differences for each new project. While inconvenient, inconsistent annotation mappings can also introduce errors, and mismatched consent frameworks can create legal and ethical exposure. The practical consequence is that most tools are trained and evaluated on a single corpus or a small subset, limiting their generalizability.  \nProblem 2: The absence of shared benchmarks slows development. Without standardized evaluation sets spanning diverse languages, child ages, and recording contexts, it is difficult to compare models across papers, identify pain points, or track progress over time. To our knowledge, no shared benchmarks currently exist for child-centered speech processing com-  \n**indicates the corresponding author.  \nparable to those available for adult speech. Importantly, this is not just a matter of data being unavailable: even for corpora with human annotations, deriving a reusable benchmark requires resolving the heterogeneity problem described above first. A benchmark built on a single corpus or English-dominant data will also give an overly optimistic picture of how well tools generalize across the linguistic and cultural diversity of LFR research contexts.  \nProblem 3: Standard ML workflows are not designed for sensitive child speech data. LFRs inevitably capture privacy-sensitive moments of people’s everyday lives and incidentally record third-party individuals who may have not consented to participate to the data collection [2] . Platforms such as HomeBank [4] and Databrary [5] have established tiered access controls for raw audio, but they ","cbCaifRYTZDXVtMd","https://ap.wps.com/l/cbCaifRYTZDXVtMd","pdf",349963,2,1,6,"English","en",105,"# Abstract\n# 1. Introduction\n## Problem 1: Cross-corpus heterogeneity\n## Problem 2: Absence of shared benchmarks\n## Problem 3: ML workflows not designed for sensitive child speech\n# 2. Background\n## Annotation cost and partial solutions","[{\"question\":\"Why are long-form recordings (LFRs) valuable for studying early language development?\",\"answer\":\"LFRs provide unmatched ecological validity because they capture child-centered audio from everyday contexts over extended periods, supporting analysis of language input, production, and acquisition.\"},{\"question\":\"What three problems limit the use of LFR corpora for speech tool development and evaluation?\",\"answer\":\"Cross-corpus heterogeneity makes joint use difficult, the lack of standardized benchmarks slows comparison across languages and conditions, and standard ML workflows do not adequately govern privacy constraints for sensitive child speech.\"},{\"question\":\"How does the proposed framework address cross-corpus heterogeneity, benchmarking, and privacy governance?\",\"answer\":\"It standardizes a collection of 27 child-centered datasets (S1), uses a replicable pipeline to derive four benchmark datasets (S2), and introduces ELSI, a role-based ecosystem that embeds ethical governance across the ML workflow (S3).\"}]",1784189832,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"deriving-benchmarking-datasets-from-long-form-recordings-challenges-and-opportunities","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/deriving-benchmarking-datasets-from-long-form-recordings-challenges-and-opportunities/83702/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-25","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},"Why are long-form recordings (LFRs) valuable for studying early language development?","Question",{"text":75,"@type":76},"LFRs provide unmatched ecological validity because they capture child-centered audio from everyday contexts over extended periods, supporting analysis of language input, production, and acquisition.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What three problems limit the use of LFR corpora for speech tool development and evaluation?",{"text":80,"@type":76},"Cross-corpus heterogeneity makes joint use difficult, the lack of standardized benchmarks slows comparison across languages and conditions, and standard ML workflows do not adequately govern privacy constraints for sensitive child speech.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed framework address cross-corpus heterogeneity, benchmarking, and privacy governance?",{"text":84,"@type":76},"It standardizes a collection of 27 child-centered datasets (S1), uses a replicable pipeline to derive four benchmark datasets (S2), and introduces ELSI, a role-based ecosystem that embeds ethical governance across the ML workflow (S3).","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":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]