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NLPerspectives","10.63317\u002F5a3bvdkzb6f7","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnlperspectives\u002F2026.nlperspectives-1.0.pdf",[2341,2342,2343,2344,2347],{"given_name":1533,"surname":1534},{"given_name":1524,"surname":1525},{"given_name":1527,"surname":1528},{"given_name":2345,"surname":2346},"Elisa","Leonardelli",{"given_name":1536,"surname":1537},{"workshop_id":2349,"year":7,"full_workshop_id":2350,"proceedings_title":2351,"paperCount":485,"doi":2352,"pdf_url":2353,"venue_ids":2354,"publisher":13,"editors":2355,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"nonliteral","lrec2026_ws_nonliteral","Proceedings of Learning Non-Literal Expressions with Small Data @ LREC 2026","10.63317\u002F24t598e89qez","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnonliteral\u002F2026.nonliteral-1.0.pdf","nonliteral|ws",[2356,2359],{"given_name":2357,"surname":2358},"Markus","Egg",{"given_name":2360,"surname":2361},"Valia","Kordoni",{"workshop_id":2363,"year":7,"full_workshop_id":2364,"proceedings_title":2365,"paperCount":2366,"doi":2367,"pdf_url":2368,"venue_ids":2369,"publisher":13,"editors":2370,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"nslp","lrec2026_ws_nslp","Proceedings of Natural Scientific Language Processing (NSLP) @ LREC 2026",29,"10.63317\u002F44i27tid8nim","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnslp\u002F2026.nslp-1.0.pdf","nslp|ws",[2371,2372,2375,2378,2381],{"given_name":717,"surname":718},{"given_name":2373,"surname":2374},"Stefan","Dietze",{"given_name":2376,"surname":2377},"Danilo","Dessi",{"given_name":2379,"surname":2380},"Diana","Maynard",{"given_name":2382,"surname":2383},"Sonja","Schimmler",{"workshop_id":794,"year":7,"full_workshop_id":2385,"proceedings_title":2386,"paperCount":2387,"doi":2388,"pdf_url":2389,"venue_ids":1548,"publisher":13,"editors":2390,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"lrec2026_ws_osact","The 7th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT7) with 5 Shared Tasks",43,"10.63317\u002F55nvfe53k6fq","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fosact\u002F2026.osact-1.0.pdf",[2391,2392,2393],{"given_name":1551,"surname":1552},{"given_name":2073,"surname":2074},{"given_name":2394,"surname":2395},"Saad","Ezzini",{"workshop_id":800,"year":7,"full_workshop_id":2397,"proceedings_title":2398,"paperCount":541,"doi":2399,"pdf_url":2400,"venue_ids":1568,"publisher":13,"editors":2401,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"lrec2026_ws_parlaclarin","Proceedings of the ParlaCLARIN V Workshop on Interoperability, Multilinguality, and Multimodality in Parliamentary Corpora","10.63317\u002F2gcgvfpyafm6","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fparlaclarin\u002F2026.parlaclarin-1.0.pdf",[2402,2403,2406],{"given_name":373,"surname":1574},{"given_name":2404,"surname":2405},"Vincent","Vandeghinste",{"given_name":1576,"surname":2407},"Bodron",{"workshop_id":990,"year":7,"full_workshop_id":2409,"proceedings_title":2131,"paperCount":2410,"doi":2411,"pdf_url":2412,"venue_ids":1583,"publisher":13,"editors":2413,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"lrec2026_ws_politicalnlp",30,"10.63317\u002F382p55orpsvc","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fpoliticalnlp\u002F2026.politicalnlp-1.0.pdf",[],{"workshop_id":2415,"year":7,"full_workshop_id":2416,"proceedings_title":2417,"paperCount":815,"doi":2418,"pdf_url":2419,"venue_ids":2420,"publisher":13,"editors":2421,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"pressmint","lrec2026_ws_pressmint","Proceedings of the First Workshop on Creating Interoperable Corpora of Historical Newspapers","10.63317\u002F4xmf6mt4ovnj","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fpressmint\u002F2026.pressmint-1.0.pdf","pressmint|ws",[2422,2425,2428],{"given_name":2423,"surname":2424},"Maciej","Ogrodniczuk",{"given_name":2426,"surname":2427},"Petya","Osenova",{"given_name":2429,"surname":2430},"Tanja","Wissik",{"workshop_id":806,"year":7,"full_workshop_id":2432,"proceedings_title":2433,"paperCount":469,"doi":2434,"pdf_url":2435,"venue_ids":1602,"publisher":13,"editors":2436,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"lrec2026_ws_rail","Proceedings of Resources for African Indigenous Languages (RAIL) 2026 @ LREC 2026","10.63317\u002F44hkfj5cg3wf","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Frail\u002F2026.rail-1.0.pdf",[],{"workshop_id":2438,"year":7,"full_workshop_id":2439,"proceedings_title":2440,"paperCount":645,"doi":2441,"pdf_url":2442,"venue_ids":2443,"publisher":13,"editors":2444,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"rapid6mentalai","lrec2026_ws_rapid6mentalai","Proceedings of the Sixth Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive\u002Fpsychiatric\u002Fdevelopmental impairments in cooperation with the MENTAL.ai consortium","10.63317\u002F54scnv3cy8x7","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Frapid6mentalai\u002F2026.rapid6mentalai-1.0.pdf","rapid6mentalai|ws",[2445,2446,2448,2451,2452,2454],{"given_name":1624,"surname":1625},{"given_name":1630,"surname":2447},"Themistocleous",{"given_name":2449,"surname":2450},"Gaël","Dias",{"given_name":1627,"surname":1628},{"given_name":1639,"surname":2453},"Öhman",{"given_name":2455,"surname":2456},"Sebastião","Pais",{"workshop_id":2458,"year":7,"full_workshop_id":2459,"proceedings_title":2460,"paperCount":712,"doi":2461,"pdf_url":2462,"venue_ids":2463,"publisher":13,"editors":2464,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"readixtsar","lrec2026_ws_readixtsar","Proceedings of the Joint Workshop on Readability and Text Simplification (READIxTSAR) @ LREC 2026","10.63317\u002F3odyoa9tpigg","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Freadixtsar\u002F2026.readixtsar-1.0.pdf","readixtsar|ws",[2465,2468,2470,2472,2474,2475,2478,2481,2484,2485],{"given_name":2466,"surname":2467},"Matthew","Shardlow",{"given_name":1723,"surname":2469},"François",{"given_name":384,"surname":2471},"Amaro",{"given_name":690,"surname":2473},"Baptista",{"given_name":1652,"surname":1653},{"given_name":2476,"surname":2477},"Eugénio","Ribeiro",{"given_name":2479,"surname":2480},"Horacio","Saggion",{"given_name":2482,"surname":2483},"Regina","Stodden",{"given_name":1655,"surname":1656},{"given_name":1650,"surname":1649},{"workshop_id":2487,"year":7,"full_workshop_id":2488,"proceedings_title":2489,"paperCount":678,"doi":2490,"pdf_url":2491,"venue_ids":2492,"publisher":13,"editors":2493,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"resourceful","lrec2026_ws_resourceful","The Fourth Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL 2026)","10.63317\u002F3mcee7ktdfxn","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fresourceful\u002F2026.resourceful-1.0.pdf","resourceful|ws",[],{"workshop_id":826,"year":7,"full_workshop_id":2495,"proceedings_title":2496,"paperCount":836,"doi":2497,"pdf_url":2498,"venue_ids":2499,"publisher":13,"editors":2500,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"lrec2026_ws_signlang","Proceedings of the LREC 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion","10.63317\u002F4zjm486botgq","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fsignlang\u002F2026.signlang-1.0.pdf","signlang|ws",[2501,2502,2503,2504,2507,2508],{"given_name":1717,"surname":1716},{"given_name":1720,"surname":1719},{"given_name":1723,"surname":1722},{"given_name":2505,"surname":2506},"Julie","A. Hochgesang",{"given_name":1729,"surname":1728},{"given_name":616,"surname":1731},{"workshop_id":1030,"year":7,"full_workshop_id":2510,"proceedings_title":2511,"paperCount":957,"doi":2512,"pdf_url":2513,"venue_ids":1738,"publisher":13,"editors":2514,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"lrec2026_ws_sigul","Proceedings of the SIGUL 2026 Joint Workshop with ELE, EURALI, and DCLRL \"Towards Inclusivity and Equality: Language Resources and Technologies for Under-Resourced and Endangered Languages","10.63317\u002F3x5d49bm2yjm","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fsigul\u002F2026.sigul-1.0.pdf",[2515,2516,2517,2518,2519,2520,2523,2524,2525],{"given_name":1246,"surname":1247},{"given_name":67,"surname":68},{"given_name":1745,"surname":1746},{"given_name":1741,"surname":1742},{"given_name":687,"surname":688},{"given_name":2521,"surname":2522},"Constantine","Lignos",{"given_name":1256,"surname":1257},{"given_name":1772,"surname":2167},{"given_name":717,"surname":718},{"workshop_id":2527,"year":7,"full_workshop_id":2528,"proceedings_title":2529,"paperCount":771,"doi":2530,"pdf_url":2531,"venue_ids":2532,"publisher":13,"editors":2533,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"slide","lrec2026_ws_slide","Proceedings of the Workshop on Structured Linguistic Data and Evaluation (SLiDE)","10.63317\u002F2ncrhaxfvhi4","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fslide\u002F2026.slide-1.0.pdf","slide|ws",[2534,2537,2540,2543,2546],{"given_name":2535,"surname":2536},"Germany)","Erhard Hinrichs (Tübingen University",{"given_name":2538,"surname":2539},"Sweden)","Joakim Nivre (Uppsala University",{"given_name":2541,"surname":2542},"Bulgaria)","Petya Osenova (Sofia University",{"given_name":2544,"surname":2545},"USA)","James Pustejovsky (Brandeis University",{"given_name":2535,"surname":2547},"Claus Zinn (Tübingen University",{"workshop_id":2549,"year":7,"full_workshop_id":2550,"proceedings_title":2551,"paperCount":469,"doi":2552,"pdf_url":2553,"venue_ids":2554,"publisher":13,"editors":2555,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"soconnlpsi","lrec2026_ws_soconnlpsi","Proceedings of the 1st Workshop on Social Context (SoCon) and the 2nd Workshop on Integrating NLP and Psychology to Study Social Interactions (NLPSI) @ LREC 2026","10.63317\u002F5qbp9pb9xpfe","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fsoconnlpsi\u002F2026.soconnlpsi-1.0.pdf","soconnlpsi|ws",[2556,2558,2559,2562,2563,2566,2569,2572,2573,2574,2577,2578,2581,2584,2587],{"given_name":1433,"surname":2557},"Antonio Stranisci",{"given_name":1984,"surname":1985},{"given_name":2560,"surname":2561},"Sofie","Labat",{"given_name":2126,"surname":2127},{"given_name":2564,"surname":2565},"Aswathy","Velutharambath",{"given_name":2567,"surname":2568},"Sabine","Weber",{"given_name":2570,"surname":2571},"Rossana","Damiano",{"given_name":1536,"surname":1537},{"given_name":61,"surname":62},{"given_name":2575,"surname":2576},"Bennett","Kleinberg",{"given_name":474,"surname":475},{"given_name":2579,"surname":2580},"Viviana","Patti",{"given_name":2582,"surname":2583},"Flor","Miriam Plaza-del-Arco",{"given_name":2585,"surname":2586},"Maarten","Sap",{"given_name":2588,"surname":2589},"Seid","Muhie Yimam",{"workshop_id":2591,"year":7,"full_workshop_id":2592,"proceedings_title":2593,"paperCount":771,"doi":2594,"pdf_url":2595,"venue_ids":2596,"publisher":13,"editors":2597,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"speakable","lrec2026_ws_speakable","Proceedings of Speech Language Models in Low-Resource Settings: Performance, Evaluation, and Bias Analysis (SPEAKABLE) @ LREC 2026","10.63317\u002F443zrkx8bhr6","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fspeakable\u002F2026.speakable-1.0.pdf","speakable|ws",[2598,2601,2604,2606,2609,2611],{"given_name":2599,"surname":2600},"Nina","Hosseini-Kivanani",{"given_name":2602,"surname":2603},"Alessio","Brutti",{"given_name":1433,"surname":2605},"Matassoni",{"given_name":2607,"surname":2608},"Sandipana","Dowerah",{"given_name":1530,"surname":2610},"Liga",{"given_name":2612,"surname":2613},"Christoph","Schommer",{"workshop_id":2615,"year":7,"full_workshop_id":2616,"proceedings_title":2617,"paperCount":2366,"doi":2618,"pdf_url":2619,"venue_ids":2620,"publisher":13,"editors":2621,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"udw","lrec2026_ws_udw","Proceedings of the Ninth Workshop on Universal            Dependencies (UDW 2026)","10.63317\u002F4c2x4v6ohrvs","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fudw\u002F2026.udw-1.0.pdf","udw|ws",[],{"workshop_id":859,"year":7,"full_workshop_id":2623,"proceedings_title":2624,"paperCount":628,"doi":2625,"pdf_url":2626,"venue_ids":1821,"publisher":13,"editors":2627,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"lrec2026_ws_wildre","Proceedings of the 8th Workshop on Indian Language Data: Resources and Evaluation","10.63317\u002F32ouujp5bxoa","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fwildre\u002F2026.wildre-1.0.pdf",[2628,2629,2630,2632],{"given_name":1824,"surname":1825},{"given_name":552,"surname":553},{"given_name":1827,"surname":2631},"L",{"given_name":2633,"surname":1783},"Devendr",{"conference_id":6,"year":7,"proceedings_title":8,"venue_ids":9,"isbn":10,"issn":11,"doi":12,"publisher":13,"editors":2635,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40,"conference_url":41,"pdf_url":42,"img_conf_url":43,"paperCount":44},[2636,2637,2638,2639,2640,2641],{"given_name":16,"surname":17},{"given_name":19,"surname":20},{"given_name":22,"surname":23},{"given_name":25,"surname":26},{"given_name":28,"surname":29},{"given_name":31,"surname":32},{"workshop":2643,"papers":2651},{"workshop_id":2002,"year":7,"full_workshop_id":2003,"proceedings_title":2004,"paperCount":1263,"doi":2005,"pdf_url":2006,"venue_ids":2007,"publisher":13,"editors":2644,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},[2645,2646,2647,2648,2649,2650],{"given_name":2010,"surname":2011},{"given_name":427,"surname":428},{"given_name":2014,"surname":2015},{"given_name":1472,"surname":2017},{"given_name":2019,"surname":2020},{"given_name":2022,"surname":2023},[2652,2667,2689,2701,2715,2741,2755,2792,2821,2837,2853,2885,2899,2913,2928,2943,2961,2991,3009,3026,3040,3054,3075,3091,3120,3148,3167,3184,3213,3233,3258,3279,3296,3313],{"paper_id":2653,"title":2654,"year":7,"month":358,"day":135,"doi":2655,"resource_url":2656,"first_page":459,"last_page":193,"pdf_url":2657,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2658,"paper_type":2659,"authors":2660,"abstract":2666},"lrec2026-ws-dialres-01","A Bolu: A Structured Dataset for the Computational Analysis of Sardinian Improvisational Poetry ","10.63317\u002F3d9ufzotw55s","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-01","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.1.pdf","calderaro-etal-2026-bolu","workshop",[2661,2664],{"paper_id":2653,"author_seq":459,"given_name":2662,"surname":2663,"affiliation":135,"orcid":135},"Silvio","Calderaro",{"paper_id":2653,"author_seq":434,"given_name":1729,"surname":2665,"affiliation":135,"orcid":135},"Monti","The growing interest of Natural Language Processing (NLP) in minority languages has not yet bridged the gap in the preservation of oral linguistic heritage. In particular, extemporaneous poetry — a performative genre based on real-time improvisation, metrical-rhetorical competence — remains a largely unexplored area of computational linguistics. This methodological gap necessitates the creation of specific resources to document and analyse the structures of improvised poetry. This is the context in which A Bolu was created, the first structured corpus of extemporaneous poetry dedicated to cantada logudorese, a variant of the Sardinian language. The dataset comprises 2,835 stanzas for a total of 141,321 tokens. The study presents the architecture of the corpus and applies a multidimensional analysis combining descriptive statistical indices and computational linguistics techniques to map the characteristics of the poetic text. The results indicate that the production of Sardinian extemporaneous poets is characterised by recurring patterns that support Parry and Lord’s theory of formulaicity. This evidence not only provides a new key to understanding oral creativity, but also offers a significant contribution to the development of NLP tools that are more inclusive and sensitive to the specificities of less widely spoken languages.",{"paper_id":2668,"title":2669,"year":7,"month":358,"day":135,"doi":2670,"resource_url":2671,"first_page":161,"last_page":2672,"pdf_url":2673,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2674,"paper_type":2659,"authors":2675,"abstract":2688},"lrec2026-ws-dialres-02","Saar-Voice: A Multi-Speaker Saarbrücken Dialect Speech Corpus ","10.63317\u002F2xvbjjktadtu","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-02","23","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.2.pdf","oberkircher-etal-2026-saar",[2676,2679,2682,2685],{"paper_id":2668,"author_seq":459,"given_name":2677,"surname":2678,"affiliation":135,"orcid":135},"Lena Sophie","Oberkircher",{"paper_id":2668,"author_seq":434,"given_name":2680,"surname":2681,"affiliation":135,"orcid":135},"Jesujoba","Alabi",{"paper_id":2668,"author_seq":408,"given_name":2683,"surname":2684,"affiliation":135,"orcid":135},"Dietrich","Klakow",{"paper_id":2668,"author_seq":387,"given_name":2686,"surname":2687,"affiliation":135,"orcid":135},"Jürgen","Trouvain","Natural language processing (NLP) and speech technologies have made significant progress in recent years; however, they remain largely focused on standardized language varieties. Dialects, despite their cultural significance and widespread use, are underrepresented in linguistic resources and computational models, resulting in performance disparities. To address this gap, we introduce Saar-Voice, a six-hour speech corpus for the Saarbrücken dialect of German. The dataset was created by first collecting text through digitized books and locally sourced materials. A subset of this text was recorded by nine speakers, and we conducted analyses on both the textual and speech components to assess the dataset’s characteristics and quality. We discuss methodological challenges related to orthographic and speaker variation, and explore grapheme-to-phoneme (G2P) conversion. The resulting corpus provides aligned textual and audio representations. This serves as a foundation for future research on dialect-aware TTS, which is proposed in the form of zero-shot and fine-tuned model adaptation in low-resource scenarios.",{"paper_id":2690,"title":2691,"year":7,"month":358,"day":135,"doi":2692,"resource_url":2693,"first_page":2694,"last_page":2695,"pdf_url":2696,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2697,"paper_type":2659,"authors":2698,"abstract":135},"lrec2026-ws-dialres-03","MD_NLP: Reconstructing an Australian English Heritage Dialect Corpus from the Mitchell-Delbridge Recordings through LLM-Assisted Speaker Attribution ","10.63317\u002F3iga3zzsh92p","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-03","24","32","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.3.pdf","coats-2026-md_nlp",[2699],{"paper_id":2690,"author_seq":459,"given_name":1913,"surname":2700,"affiliation":135,"orcid":135},"Coats",{"paper_id":2702,"title":2703,"year":7,"month":358,"day":135,"doi":2704,"resource_url":2705,"first_page":2706,"last_page":2707,"pdf_url":2708,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2709,"paper_type":2659,"authors":2710,"abstract":2714},"lrec2026-ws-dialres-04","Challenges in the Detection of Dialect for Historical Languages; the Case of Old Irish Text Resources ","10.63317\u002F2zf2k5jdm74v","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-04","33","47","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.4.pdf","doyle-2026-challenges",[2711],{"paper_id":2702,"author_seq":459,"given_name":2712,"surname":2713,"affiliation":135,"orcid":135},"Adrian","Doyle","Old Irish presents particular challenges for the study of automatic dialect detection. It is generally accepted that Old Irish presents little trace of dialect. Extant Old Irish text resources introduce a considerable amount of extra variation, which could impact dialect identification applications. While some scholarship has suggested that certain features may be indicative of dialect, such hypotheses are difficult to substantiate where authorship is anonymous, or where the text itself is not associated with a particular geographical region. This paper describes the application of stylometric dialect detection techniques to Old Irish texts, and discusses the features which emerge from this process as potential markers of dialect. The aim is not necessarily to identify Old Irish dialectal features, but rather to investigate the impact that Old Irish text resources could have on such applications. This paper does, however, add to the extant body of research by highlighting some features which might be identified as stylistically distinct by stylometric dialect identification techniques.",{"paper_id":2716,"title":2717,"year":7,"month":358,"day":135,"doi":2718,"resource_url":2719,"first_page":2720,"last_page":2721,"pdf_url":2722,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2723,"paper_type":2659,"authors":2724,"abstract":2740},"lrec2026-ws-dialres-05","Phonologically-aware Automatic Speech Recognition Evaluation of Low-Resource Languages: The Case of Basque Dialects ","10.63317\u002F262fznwr54us","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-05","48","57","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.5.pdf","souganidis-etal-2026-phonologically",[2725,2728,2731,2734,2737],{"paper_id":2716,"author_seq":459,"given_name":2726,"surname":2727,"affiliation":135,"orcid":135},"Christoforos","Souganidis",{"paper_id":2716,"author_seq":434,"given_name":2729,"surname":2730,"affiliation":135,"orcid":135},"Asier","Herranz",{"paper_id":2716,"author_seq":408,"given_name":2732,"surname":2733,"affiliation":135,"orcid":135},"Ibon","Saratxaga",{"paper_id":2716,"author_seq":387,"given_name":2735,"surname":2736,"affiliation":135,"orcid":135},"Eva","Navas",{"paper_id":2716,"author_seq":358,"given_name":2738,"surname":2739,"affiliation":135,"orcid":135},"Inma","Hernaez","Automatic speech recognition models are typically trained with data of standard languages. However, their performance degrades when dealing with non-standard dialectal speech. In this paper, we present the first evaluation of an automatic speech recognition system for Basque, a low-resource language, based on spontaneous broadcast speech with high representation of dialectal speech. It relies on a 140-h manually annotated propietary corpus of television programs broadcast by Basque Radio Television, including dialect-level labels, as well as standardized and pseudo-phonetic transcriptions. We find that recognition performance significantly degrades for dialectal compared to standard speech, for all dialects present in our corpus. Subsequently, we provide a quantitative analysis of phonological phenomena based on single-word substitution errors, and identify 52 recurrent phenomena, grouped into sound deletions, epentheses, and substitutions. We further show a modest but statistically significant correlation between the number of phonological phenomena in an utterance and its recognition error rate. Our findings highlight the limitations of dialect-agnostic evaluation and motivate linguistically informed, dialect-aware strategies for automatic speech recognition in low-resource and typologically diverse languages.",{"paper_id":2742,"title":2743,"year":7,"month":358,"day":135,"doi":2744,"resource_url":2745,"first_page":2746,"last_page":2747,"pdf_url":2748,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2749,"paper_type":2659,"authors":2750,"abstract":2754},"lrec2026-ws-dialres-06","Systematic Normalization of Spoken Mixed-Language, Mixed-Dialect Data ","10.63317\u002F3bv9dmxr24p6","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-06","58","69","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.6.pdf","blevins-2026-systematic",[2751],{"paper_id":2742,"author_seq":459,"given_name":2752,"surname":2753,"affiliation":135,"orcid":135},"Margaret","Blevins","Literary transcriptions of spoken language often deviate from standard, written language. These variations can lead to higher than desirable error rates in NLP processing. This is particularly the case for spoken data of low resource varieties, including dialects and contact varieties of higher resource languages. This paper outlines a proposal for the systematic dialect-to-standard normalization of spoken language from language contact and dialect contact situations. This system is then tested on the Texas German Sample Corpus ( 13 hours), a set of audio and transcripts of Texas German conversations. Texas German is an umbrella term for a set of a heritage varieties of German spoken in Texas, USA that descend from multiple German dialects and that have been in contact with English for 150+ years. The proposed normalization system, along with the accompanying language-tagging system, can act as a starting point for other projects interested in normalizing their mixed variety data.",{"paper_id":2756,"title":2757,"year":7,"month":358,"day":135,"doi":2758,"resource_url":2759,"first_page":2760,"last_page":2761,"pdf_url":2762,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2763,"paper_type":2659,"authors":2764,"abstract":2791},"lrec2026-ws-dialres-07","Handling Cross-Dialect Syntactic Variation: a Theory-Driven Web Resource ","10.63317\u002F4x8k8si3eybn","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-07","70","82","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.7.pdf","lidestri-etal-2026-handling",[2765,2768,2770,2773,2776,2779,2782,2785,2787,2789],{"paper_id":2756,"author_seq":459,"given_name":2766,"surname":2767,"affiliation":135,"orcid":135},"Emanuela","Li Destri",{"paper_id":2756,"author_seq":434,"given_name":1433,"surname":2769,"affiliation":135,"orcid":135},"Longhin",{"paper_id":2756,"author_seq":408,"given_name":2771,"surname":2772,"affiliation":135,"orcid":135},"Gaia","Sorge",{"paper_id":2756,"author_seq":387,"given_name":2774,"surname":2775,"affiliation":135,"orcid":135},"Sofia","Ferroni",{"paper_id":2756,"author_seq":358,"given_name":2777,"surname":2778,"affiliation":135,"orcid":135},"Giovanni Battista","Matteazzi",{"paper_id":2756,"author_seq":333,"given_name":2780,"surname":2781,"affiliation":135,"orcid":135},"Andrea","Artioli",{"paper_id":2756,"author_seq":309,"given_name":2783,"surname":2784,"affiliation":135,"orcid":135},"Lorenzo","Carletti",{"paper_id":2756,"author_seq":280,"given_name":1755,"surname":2786,"affiliation":135,"orcid":135},"Motta",{"paper_id":2756,"author_seq":252,"given_name":1694,"surname":2788,"affiliation":135,"orcid":135},"Longobardi",{"paper_id":2756,"author_seq":224,"given_name":1592,"surname":2790,"affiliation":135,"orcid":135},"Guardiano","Cross-dialect syntactic variation tests the limits of comparative analysis, owing to the entanglement of inheritance and contact in dialect systems. Addressing this challenge requires analytical tools combining the theoretical depth of formal models of grammatical competence with quantitative taxonomic techniques. The Parametric Comparison Method (PCM) embodies this integration by quantifying structural similarity across grammars through the comparison of abstract syntactic rules. The method has been shown to achieve a good degree of resolution in dialectal domains, capturing subtle contrasts and yielding configurations aligning with phylogenetic expectations while remaining sensitive to contact-induced convergence. Fully assessing its effectiveness as a resource for the quantitative study of syntactic dialectology, however, requires an infrastructure that ensures systematic data collection, consistent parameter setting, and robust statistical evaluation across diverse datasets. The PCM Hub is a web-based resource designed for this purpose. It integrates guided elicitation, automated parameter-setting procedures, data management, and the computation of distances and automatic classifications within a unified environment. By standardizing the transition from raw linguistic observations to a structured, replicable empirical apparatus, the PCM Hub provides the practical and quantitative support necessary to test the power of the PCM across expanded comparative domains.",{"paper_id":2793,"title":2794,"year":7,"month":358,"day":135,"doi":2795,"resource_url":2796,"first_page":2797,"last_page":2798,"pdf_url":2799,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2800,"paper_type":2659,"authors":2801,"abstract":2820},"lrec2026-ws-dialres-08","Can LLM Agents Identify Spoken Dialects like a Linguist? ","10.63317\u002F27m2tbgjcat8","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-08","83","92","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.8.pdf","bystrich-etal-2026-can",[2802,2805,2808,2811,2814,2817],{"paper_id":2793,"author_seq":459,"given_name":2803,"surname":2804,"affiliation":135,"orcid":135},"Tobias","Bystrich",{"paper_id":2793,"author_seq":434,"given_name":2806,"surname":2807,"affiliation":135,"orcid":135},"Lukas","Hamm",{"paper_id":2793,"author_seq":408,"given_name":2809,"surname":2810,"affiliation":135,"orcid":135},"Maria Hassan","Akhter",{"paper_id":2793,"author_seq":387,"given_name":2812,"surname":2813,"affiliation":135,"orcid":135},"Lea","Fischbach",{"paper_id":2793,"author_seq":358,"given_name":2815,"surname":2816,"affiliation":135,"orcid":135},"Lucie","Flek",{"paper_id":2793,"author_seq":333,"given_name":2818,"surname":2819,"affiliation":135,"orcid":135},"Akbar","Karimi","Due to the scarcity of labeled dialectal speech, audio dialect classification is a challenging task for most languages, including Swiss German. In this work, we explore the ability of large language models (LLMs) as agents in understanding the dialects and whether they can show comparable performance to models such as HuBERT in dialect classification. In addition, we provide an LLM baseline and a human linguist one. Our approach uses phonetic transcriptions produced by ASR systems and combines them with linguistic resources such as dialect feature maps, vowel history, and rules. Our findings indicate that, when linguistic information is provided, the LLM predictions improve. The human baseline shows that automatically generated transcriptions can be beneficial for such classifications, but also present opportunities for improvement.",{"paper_id":2822,"title":2823,"year":7,"month":358,"day":135,"doi":2824,"resource_url":2825,"first_page":2826,"last_page":2827,"pdf_url":2828,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2829,"paper_type":2659,"authors":2830,"abstract":2836},"lrec2026-ws-dialres-09","Beyond Accuracy: Analyzing Dialect Confusion in Automatic Speech-Based Dialect Classification ","10.63317\u002F5f65nkr6qreo","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-09","93","103","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.9.pdf","fischbach-etal-2026-beyond",[2831,2832,2835],{"paper_id":2822,"author_seq":459,"given_name":2812,"surname":2813,"affiliation":135,"orcid":135},{"paper_id":2822,"author_seq":434,"given_name":2833,"surname":2834,"affiliation":135,"orcid":135},"Alfred","Lameli",{"paper_id":2822,"author_seq":408,"given_name":2815,"surname":2816,"affiliation":135,"orcid":135},"Automatic dialect classification is commonly treated as a supervised task with a primary focus on overall accuracy. In this paper, we argue that classification errors and model uncertainty provide valuable insights into dialectal structure and variation. We analyze a speech-based dialect classification model trained on German dialect data from three generations and evaluated across 250 speaker-disjoint splits (median weighted F1=0.42). A systematic confusion analysis shows that misclassifications are largely explained by speaker diversity, dialectal similarity, geographical proximity, and speaker self-assessment. Among these factors, the number of speakers per dialect has the strongest impact on performance, while frequent confusions between closely related dialects reflect inherent linguistic similarity rather than model limitations. Generational analyses further indicate that younger speakers exhibit reduced dialectal distinctiveness, although core dialectal features remain shared across generations. By explicitly modeling classification uncertainty, the proposed approach enables the analysis of dialect transition areas and gradient dialect boundaries. Overall, this work demonstrates that automatic dialect classification can serve not only as a predictive task but also as a tool for dialectological analysis.",{"paper_id":2838,"title":2839,"year":7,"month":358,"day":135,"doi":2840,"resource_url":2841,"first_page":2842,"last_page":2843,"pdf_url":2844,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2845,"paper_type":2659,"authors":2846,"abstract":2852},"lrec2026-ws-dialres-10","FLEURS-Kobani: Extending FLEURS dataset for Northern Kurdish ","10.63317\u002F267vdxnysvbe","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-10","104","109","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.10.pdf","jaff-etal-2026-fleurs",[2847,2850],{"paper_id":2838,"author_seq":459,"given_name":2848,"surname":2849,"affiliation":135,"orcid":135},"Daban Q.","Jaff",{"paper_id":2838,"author_seq":434,"given_name":1292,"surname":2851,"affiliation":135,"orcid":135},"Mohammadamini","We present FLEURS-Kobani, a Northern Kurdish (ISO 639-3 KMR) spoken extension of FLEURS benchmark. Although FLEURS offers n-way parallel speech for 100+ languages, Northern Kurdish is absent, limits benchmarking automatic speech recognition and speech translation tasks for this language. The FLEURS-Kobani dataset consists of 5,162 validated utterances, totaling 18 hours and 24 minutes. As baselines, we fine-tuned Whisper v3-large for ASR and E2E S2TT. A two-stage fine-tuning strategy (Common Voice→FLEURS-Kobani) yields the best ASR performance (WER 28.11, CER 9.84 on test). For end-to-end S2TT (KMR→EN), Whisper achieves 8.68 BLEU on test; we additionally report pivot-derived targets and a cascaded S2TT setup. FLEURS-Kobani provides the first Northern Kurdish public benchmark for evaluation of ASR, S2TT and S2ST tasks. The data can be accessed (LINK IS BLANK DUE TO REVISION RULES) under CC BY 4.0 license.",{"paper_id":2854,"title":2855,"year":7,"month":358,"day":135,"doi":2856,"resource_url":2857,"first_page":2858,"last_page":2859,"pdf_url":2860,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2861,"paper_type":2659,"authors":2862,"abstract":2884},"lrec2026-ws-dialres-11","Exploring the reusability of Northern Kurdish resources for Badini speech recognition ","10.63317\u002F2gzjngtqiqp7","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-11","110","115","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.11.pdf","mohammadamini-etal-2026-exploring",[2863,2864,2867,2869,2872,2875,2878,2881],{"paper_id":2854,"author_seq":459,"given_name":1292,"surname":2851,"affiliation":135,"orcid":135},{"paper_id":2854,"author_seq":434,"given_name":2865,"surname":2866,"affiliation":135,"orcid":135},"Aveen Jalal","Mohammed",{"paper_id":2854,"author_seq":408,"given_name":2868,"surname":2866,"affiliation":135,"orcid":135},"Barzan Hussein",{"paper_id":2854,"author_seq":387,"given_name":2870,"surname":2871,"affiliation":135,"orcid":135},"Dezheen H.","Abdulazeez",{"paper_id":2854,"author_seq":358,"given_name":2873,"surname":2874,"affiliation":135,"orcid":135},"Imad Saeed","Sadeeq",{"paper_id":2854,"author_seq":333,"given_name":2876,"surname":2877,"affiliation":135,"orcid":135},"Dilgash Mohammed","Salih",{"paper_id":2854,"author_seq":309,"given_name":2879,"surname":2880,"affiliation":135,"orcid":135},"Amera Ismail","Melhum",{"paper_id":2854,"author_seq":280,"given_name":2882,"surname":2883,"affiliation":135,"orcid":135},"Abuobaida Abdullah","Dheyab","Badini is a variant of the Kurdish language spoken in the Duhok province of the Kurdistan Region of Iraq. It is written mainly in a modified version of the Arabic script. Although it shares the same script as Central Kurdish (CKB), it is linguistically classified under the Northern Kurdish (KMR) branch. In this paper, we explore the potential and limitations of Northern Kurdish ASR resources for the Badini variant. Firstly, we transliterate the Common Voice 18 dataset from the Latin script into the modified Arabic script and revised it to align with the orthographic conventions of Badini variant. Additionally, we introduce the first text collection for the Badini variant, containing 14,22 million tokens, which serves as a source for speech synthesis. A third resource developed in this research is a standard speech recognition benchmark recorded by 5 speakers which includes 2 hours and 46 minutes of multi-domain read speech. Results show that combining transliterated and synthetic data significantly improves recognition accuracy, achieving a 6.8% CER and 34% WER. All three resources curated during this research will be made available under the CC BY-NC-ND 4.0 license.",{"paper_id":2886,"title":2887,"year":7,"month":358,"day":135,"doi":2888,"resource_url":2889,"first_page":2890,"last_page":2891,"pdf_url":2892,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2893,"paper_type":2659,"authors":2894,"abstract":2898},"lrec2026-ws-dialres-12","Wancho Dialectometry: Community-created data and the Living Dictionaries project ","10.63317\u002F4guwk869z8we","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-12","116","123","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.12.pdf","vandam-2026-wancho",[2895],{"paper_id":2886,"author_seq":459,"given_name":2896,"surname":2897,"affiliation":135,"orcid":135},"Kellen Parker","van Dam","Community-created lexical resources for under-documented languages represent an underexplored data source for computational dialectology. This study evaluates the viability of such data for dialectometric analysis, using the Wancho (Glottocode: wanc1238) LivingDictionaries project as a case study. Wancho is a Tibeto-Burman language of the Southwestern Patkaian branch, spoken primarily in Longding District, Arunachal Pradesh, India. The dictionary is notable for being entirely community-built and speaker-facing, and uniquely among resources for Northeast India, it incorporates dialect-specific forms spanning village-level geolects and clanlects. We extract dialectal data via automated web scraping and apply a series of preprocessing steps to address inconsistencies in transcription, language labelling, and concept assignment. Pairwise linguistic distances are then computed using Sound Class Alignment (SCA, List 2010), which captures phonological similarity more accurately than raw edit distance by incorporating articulatory feature structure. The resulting distance matrix is analysed through UPGMA hierarchical clustering and NeighborNet split network inference. Despite the dataset’s uneven dialect coverage and absence of systematic cognate coding, SCA-based distances recover the traditional Upper\u002FLower\u002FMiddle Wancho distinction and correctly situate transitional varieties. These results hold even for dialects with as few as a dozen attested forms. We show that unlike Bayesian phylogenetic inference which is poorly suited to data of this density and distribution, SCA proves to be a reliable metric. Our findings suggest that SCA distance is robust to the kinds of noise and sparsity characteristic of community-generated lexical data, and that such resources constitute a viable, if imperfect, input for automated dialectometric workflows — particularly in contexts where fieldwork-based data collection is not currently feasible.",{"paper_id":2900,"title":2901,"year":7,"month":358,"day":135,"doi":2902,"resource_url":2903,"first_page":2904,"last_page":2905,"pdf_url":2906,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2907,"paper_type":2659,"authors":2908,"abstract":2912},"lrec2026-ws-dialres-13","Dialectometry and Evaluation of the ePark Corpus for Low-Resource Formosan Language Dialects ","10.63317\u002F4scoopyavtvi","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-13","124","134","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.13.pdf","gagnier-2026-dialectometry",[2909],{"paper_id":2900,"author_seq":459,"given_name":2910,"surname":2911,"affiliation":135,"orcid":135},"Henry","Gagnier","Formosan languages are a critically endangered branch of the Austronesian family spoken in Taiwan, and many of their dialects remain poorly understood and computationally understudied. Subgrouping relationships in these languages are often contested and unresolved. We provide the first evaluation of the ePark corpus as a dialectal NLP resource, identifying its strengths and gaps for future NLP work, and present the first large-scale corpus-based computational analysis of dialect similarity across all officially recognized Formosan languages. We use the ePark corpus to analyze 42 dialects in 16 Formosan languages, and through word-level TF-IDF cosine similarity, Jaccard similarity over shared vocabulary, and Levenshtein distance, we quantify pairwise dialectal relationships within the Amis, Atayal, Seediq, Bunun, Paiwan, Rukai, and Puyuma languages. We find that simple lexical similarity methods can recover and confirm linguistically established dialectal subgroupings. We find that in multiple cases the two metrics diverge, offering insights on contested subgroupings such as Mantauran Rukai. This work establishes a scalable methodological framework for dialectometry in low-resource languages, demonstrates the value of the ePark corpus for Formosan NLP research, and encourages future work in NLP on Formosan dialects.",{"paper_id":2914,"title":2915,"year":7,"month":358,"day":135,"doi":2916,"resource_url":2917,"first_page":2918,"last_page":2919,"pdf_url":2920,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2921,"paper_type":2659,"authors":2922,"abstract":2927},"lrec2026-ws-dialres-14","A Dialectal Corpus for Ukrainian: Collection, Classification, and Standardization ","10.63317\u002F4mkaru7y2op5","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-14","135","143","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.14.pdf","frund-etal-2026-dialectal",[2923,2926],{"paper_id":2914,"author_seq":459,"given_name":2924,"surname":2925,"affiliation":135,"orcid":135},"Yuliia","Frund",{"paper_id":2914,"author_seq":434,"given_name":693,"surname":694,"affiliation":135,"orcid":135},"Ukrainian dialects remain largely excluded from the digital linguistic landscape despite their active everyday use. We present a regional dialect corpus covering 18 administrative regions of Ukraine, compiled from digitized fieldwork collections and an online dialect atlas. The corpus comprises over 284,000 tokens of dialect text, annotated by region and partially accompanied by manually standardized translations. Using these resources, we investigate language identification and dialect-to-standard standardization. Baseline language identification yields an F-score of 0.75, rising to 0.99 with dialect-inclusive training. Dialect classification reaches 0.58, with confusion patterns reflecting known regional boundaries. For standardization, the best-performing LLM achieves a COMET score of 0.80, though BLEU scores remain low (0.21–0.23) across all models. We release the corpus, labelled datasets, model outputs, and reference translations to support future work on inclusive language technologies for non-standard varieties.",{"paper_id":2929,"title":2930,"year":7,"month":358,"day":135,"doi":2931,"resource_url":2932,"first_page":2933,"last_page":2934,"pdf_url":2935,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2936,"paper_type":2659,"authors":2937,"abstract":2942},"lrec2026-ws-dialres-15","German Dialects Across Situations, Generations, and Regions: The REDE corpus as an Oral Resource for NLP ","10.63317\u002F4fe4dkefqah9","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-15","144","152","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.15.pdf","fischer-etal-2026-german",[2938,2941],{"paper_id":2929,"author_seq":459,"given_name":2939,"surname":2940,"affiliation":135,"orcid":135},"Hanna","Fischer",{"paper_id":2929,"author_seq":434,"given_name":2833,"surname":2834,"affiliation":135,"orcid":135},"Recent advances in speech and language technologies increasingly rely on large and diverse corpora that represent linguistic variation across dialect regions, communicative situations, and social speaker characteristics. While substantial resources are available for Standard German, comparable spoken corpora for German dialects have so far been largely lacking, limiting the development and evaluation of dialect-sensitive NLP systems. The REDE corpus addresses this gap by providing a methodologically uniform collection of spoken German for 148 locations that systematically covers all major dialect areas in Germany. It comprises contemporary recordings collected in multiple elicitation and interaction settings, capturing variation across speaking styles, situational contexts, and speaker generations. With more than 1,500 hours of speech and rich metadata on regional and social dimensions, the REDE corpus constitutes a large-scale oral resource suitable for both linguistic research and NLP applications. This paper presents the design, structure, and methodological foundations of the corpus and discusses its relevance for current speech technology requirements.",{"paper_id":2944,"title":2945,"year":7,"month":358,"day":135,"doi":2946,"resource_url":2947,"first_page":2948,"last_page":2949,"pdf_url":2950,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2951,"paper_type":2659,"authors":2952,"abstract":2960},"lrec2026-ws-dialres-16","A Catalog of Basque Dialectal Resources: Online Collections and Standard-to-Dialectal Adaptations ","10.63317\u002F3zhrab5powcg","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-16","153","164","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.16.pdf","bengoetxea-etal-2026-catalog",[2953,2956,2958],{"paper_id":2944,"author_seq":459,"given_name":2954,"surname":2955,"affiliation":135,"orcid":135},"Jaione","Bengoetxea",{"paper_id":2944,"author_seq":434,"given_name":1761,"surname":2957,"affiliation":135,"orcid":135},"Gonzalez-Dios",{"paper_id":2944,"author_seq":408,"given_name":1650,"surname":2959,"affiliation":135,"orcid":135},"Agerri","Recent research on dialectal NLP has identified data scarcity as a primary limitation. To address this limitation, this paper presents a catalog of contemporary Basque dialectal data and resources, offering a systematic and comprehensive compilation of the dialectal data currently available in Basque. Two types of data sources have been distinguished: online data originally written in some dialect, and standard-to-dialect adapted data. The former includes all dialectal data that can be found online, such as news and radio sites, informal tweets, as well as online resources such as dictionaries, atlases, grammar rules, or videos. The latter consists of data that has been adapted from the standard variety to dialectal varieties, either manually or automatically. Regarding the manual adaptation, the test split of the XNLI Natural Language Inference dataset was manually adapted into three Basque dialects: Western, Central, and Navarrese-Lapurdian, yielding a high-quality parallel gold standard evaluation dataset. With respect to the automatic dialectal adaptation, the automatically adapted physical commonsense dataset (BasPhyCowest) underwent additional manual evaluation by native speakers to assess its quality and determine whether it could serve as a viable substitute for full manual adaptation (i.e., silver data creation).",{"paper_id":2962,"title":2963,"year":7,"month":358,"day":135,"doi":2964,"resource_url":2965,"first_page":2966,"last_page":2967,"pdf_url":2968,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2969,"paper_type":2659,"authors":2970,"abstract":2990},"lrec2026-ws-dialres-17","WoVis: Interactive Visualization of Word Embeddings for Semantic Change in Historical and Dialectal Language Resources ","10.63317\u002F3jzkx999kfxq","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-17","165","176","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.17.pdf","mileti-etal-2026-wovis",[2971,2974,2977,2980,2983,2986,2988],{"paper_id":2962,"author_seq":459,"given_name":2972,"surname":2973,"affiliation":135,"orcid":135},"Filip","Miletić",{"paper_id":2962,"author_seq":434,"given_name":2975,"surname":2976,"affiliation":135,"orcid":135},"Maximilian","Henkel",{"paper_id":2962,"author_seq":408,"given_name":2978,"surname":2979,"affiliation":135,"orcid":135},"Rene","Cutura",{"paper_id":2962,"author_seq":387,"given_name":2981,"surname":2982,"affiliation":135,"orcid":135},"Sophie","Sadler",{"paper_id":2962,"author_seq":358,"given_name":2984,"surname":2985,"affiliation":135,"orcid":135},"Quynh Quang","Ngo",{"paper_id":2962,"author_seq":333,"given_name":1124,"surname":2987,"affiliation":135,"orcid":135},"Sedlmair",{"paper_id":2962,"author_seq":309,"given_name":2567,"surname":2989,"affiliation":135,"orcid":135},"Schulte im Walde","Computational modeling of language variation and change often relies on comparisons of word embeddings induced from existing historical and dialectal language resources. However, their use in the wider linguistics research community and in application domains such as lexicography is challenged by their limited manipulability for non-technical users, which in turn exacerbates the underuse of such resources. Aiming to foster a broader uptake of embedding-based analyses, we introduce WoVis, an interactive visualization tool designed to compare word embedding models in analyses of semantic change. Our system supports simultaneous model comparisons along two dimensions (e.g., language varieties and time periods) and provides analyses at different levels of granularity: an overview of the full vocabulary across all word embedding models, distributional behavior of individual words, targeted comparisons of word pairs, and model-external lexical features such as frequency and affective norms. We illustrate the utility of our system on two languages, German and English, with analyses of word usage across language varieties as well as time: West vs. East Germany, 1950–1989; and general-domain US vs. scientific UK English, ca. 1800–2000.",{"paper_id":2992,"title":2993,"year":7,"month":358,"day":135,"doi":2994,"resource_url":2995,"first_page":2996,"last_page":2997,"pdf_url":2998,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2999,"paper_type":2659,"authors":3000,"abstract":3008},"lrec2026-ws-dialres-18","Speaker Normalization via Voice Conversion Reveals a Human-Machine Dissociation in Dialect Classification ","10.63317\u002F3sqk7nxsikhp","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-18","177","187","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.18.pdf","kleen-etal-2026-speaker",[3001,3004,3005,3006,3007],{"paper_id":2992,"author_seq":459,"given_name":3002,"surname":3003,"affiliation":135,"orcid":135},"Caroline","Kleen",{"paper_id":2992,"author_seq":434,"given_name":2812,"surname":2813,"affiliation":135,"orcid":135},{"paper_id":2992,"author_seq":408,"given_name":2818,"surname":2819,"affiliation":135,"orcid":135},{"paper_id":2992,"author_seq":387,"given_name":2815,"surname":2816,"affiliation":135,"orcid":135},{"paper_id":2992,"author_seq":358,"given_name":2833,"surname":2834,"affiliation":135,"orcid":135},"This study evaluates whether Retrieval-based Voice Conversion (RVC) can be used to normalize speaker-specific variability while preserving dialect-relevant acoustic cues, and what the response of human and machine systems to this manipulation reveals about the architecture of dialect recognition. In two perception experiments, speech samples from nine German dialect regions were presented either in their original form or after conversion to a single target speaker. We compared overall accuracy, confusion structures, item-level response distributions, and the interaction between listener origin and target dialect across conditions. Human classification remained stable under voice conversion. Accuracy did not differ between conditions, confusion matrices were highly correlated, and item-level divergences were minimal. The interaction between listener origin and target dialect—reflecting systematic regional bias—remained invariant. These findings indicate that RVC does not distort perceptually relevant dialectal cues and that human dialect recognition is robust to speaker normalization. In contrast, we evaluated a deep learning model under matched conditions: model accuracy improved significantly under RVC, while human performance remained unchanged. This dissociation reframes RVC as an experimental probe for investigating the divergence between human and machine speech processing, suggesting that this divergence is rooted in fundamentally different representational architectures.",{"paper_id":3010,"title":3011,"year":7,"month":358,"day":135,"doi":3012,"resource_url":3013,"first_page":3014,"last_page":3015,"pdf_url":3016,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3017,"paper_type":2659,"authors":3018,"abstract":3025},"lrec2026-ws-dialres-19","South Tyrolean Dialect-to-Standard Speech Translation: A Resource ","10.63317\u002F3visgk9f8s7z","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-19","188","194","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.19.pdf","franzini-etal-2026-south",[3019,3022],{"paper_id":3010,"author_seq":459,"given_name":3020,"surname":3021,"affiliation":135,"orcid":135},"Greta H.","Franzini",{"paper_id":3010,"author_seq":434,"given_name":3023,"surname":3024,"affiliation":135,"orcid":135},"Luca","Ducceschi","This paper presents a developing oral resource for South Tyrolean, a German dialect spoken in Northern Italy. The dialect is ubiquitous in spoken communication but lacks a standardised orthography. In this context, strict transcription into dialect is of limited to no utility to the local community. Instead, there is a distinct and strong demand for technology capable of directly translating spoken dialect into Standard German. To address this specific need, we introduce a dynamic, incrementally growing dataset designed to fine-tune ASR models for this translation task. Our corpus aggregates diverse sources, including media and research interviews, totalling over 13 hours of aligned audio. We describe a collaborative workflow where community partners contribute audio archives in exchange for automated transcriptions, creating a virtuous cycle of data improvement. Additionally, we detail our iterative model fine-tuning strategy, data collection challenges and the resulting improvements in model performance.",{"paper_id":3027,"title":3028,"year":7,"month":358,"day":135,"doi":3029,"resource_url":3030,"first_page":3031,"last_page":3032,"pdf_url":3033,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3034,"paper_type":2659,"authors":3035,"abstract":3039},"lrec2026-ws-dialres-20","TransVar – the Corpus for Variation and Change Study of the Historical Transcarpathian lects ","10.63317\u002F4wf34recmurr","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-20","195","208","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.20.pdf","afanasev-2026-transvar",[3036],{"paper_id":3027,"author_seq":459,"given_name":3037,"surname":3038,"affiliation":135,"orcid":135},"Ilia","Afanasev","The paper introduces TransVar – the corpus of the historical Transcarpathian lects (the first half of the XX century, the territories of modern Ukraine, Poland, Slovakia, and Romania). The corpus contains data from Lemko, Bojko and Hutsul small territorial lect groups. It is crucial for studies of the people of these territories, who witnessed forceful deportation from their homeland in the 1940s – 1950s, soon after the recordings were made (1920s – 1930s). The article also provides a brief overview of their linguistic properties, as evident in the material. The corpus is morphosyntactically tagged. It contains data on part-of-speech, morphological features, lemmata and syntactical dependencies. The study stresses the crux of manual analysis of the errata made in an automatic tagging phase for further improvement. The supplementary information includes named entities encountered in the text and the basic vocabulary. All the texts are accompanied by metalinguistic information, required for the sociolinguistic study. After the analysis of the current stage of the corpus creation, the article outlines further research prospects. Apart from more thorough manual annotation, one of the prospects is to add English translation with the purpose of making the material more accessible to scholars without a background in Slavic studies.",{"paper_id":3041,"title":3042,"year":7,"month":358,"day":135,"doi":3043,"resource_url":3044,"first_page":3045,"last_page":3046,"pdf_url":3047,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3048,"paper_type":2659,"authors":3049,"abstract":3053},"lrec2026-ws-dialres-21","The Generator-Eraser Paradox: Community Guidelines for Responsible LLM-Assisted Dialect Resource Creation ","10.63317\u002F3kcvyhsgnv3u","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-21","209","220","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.21.pdf","zaghouani-2026-generator",[3050],{"paper_id":3041,"author_seq":459,"given_name":3051,"surname":3052,"affiliation":135,"orcid":135},"Wajdi","Zaghouani","Dialect resources occupy a unique position at the intersection of scientific description, cultural preservation, and computational infrastructure. Large language models offer powerful capabilities for accelerating dialect resource development through retrieval-grounded drafting, corpus navigation, metadata enrichment, and annotation workflow support. However, the same systems pose substantial risks: they can contribute to dialect erasure by privileging prestige varieties, homogenizing orthography, and enabling synthetic feedback loops that reduce linguistic diversity over time. These risks are particularly acute for language varieties characterized by diglossia, limited written standardization, or marginalized speaker communities. This paper makes three contributions. First, we integrate insights from variationist sociolinguistics and corpus linguistics to formalize the generator-eraser paradox as a theoretical framework for understanding the dual nature of LLM-assisted dialect work. Second, we derive 12 community guidelines that operationalize this framework into implementable design requirements for dialect resource creation and documentation. Third, we provide an in-depth case study of Arabic dialects, including a structured comparison of widely used resources, to demonstrate how these guidelines address language-specific challenges including diglossia, orthographic variability, and community governance. The contribution is conceptual and operational rather than experimental, with the goal of enabling dialect communities and resource builders across languages to adopt LLMs without sacrificing authenticity, variation, or sovereignty.",{"paper_id":3055,"title":3056,"year":7,"month":358,"day":135,"doi":3057,"resource_url":3058,"first_page":3059,"last_page":3060,"pdf_url":3061,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3062,"paper_type":2659,"authors":3063,"abstract":3074},"lrec2026-ws-dialres-22","The Texas German Dialect Project Corpus as a Diachronic Resource for Investigating Language Contact ","10.63317\u002F3s622jzdgwuj","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-22","221","229","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.22.pdf","schmidt-etal-2026-texas",[3064,3066,3068,3071],{"paper_id":3055,"author_seq":459,"given_name":1723,"surname":3065,"affiliation":135,"orcid":135},"Schmidt",{"paper_id":3055,"author_seq":434,"given_name":3067,"surname":2753,"affiliation":135,"orcid":135},"Margaret M.",{"paper_id":3055,"author_seq":408,"given_name":3069,"surname":3070,"affiliation":135,"orcid":135},"Hans C.","Boas",{"paper_id":3055,"author_seq":387,"given_name":3072,"surname":3073,"affiliation":135,"orcid":135},"Glenn","Gilbert","The Texas German Dialect Project (TGDP) is a long-standing effort to document the unique variety of German spoken in Texas since the 1840s. For 25 years, the TGDP has built up the freely accessible Texas German Dialect Archive Online (TGDA Online) with recordings and annotations of interviews and language tasks conducted between 2001 and today with some of the last speakers of the variety, which is expected to go extinct within the next 5-10 years. The present paper reports on the most recent addition of to the TGDP’s online corpus platform— a collection of Texas German data recorded in the 1960s—as well as historical translation elicitations that will be released later in 2026. Both the contemporary and the historical materials follow very similar elicitation methods and are processed using the same pipelines, increasing their comparability. This provides a comparable historical dimension to the resource, enabling diachronic and multidimensional analyses of this endangered variety. These data can help shed light on the dynamics of language contact, dialect contact, and language death.",{"paper_id":3076,"title":3077,"year":7,"month":358,"day":135,"doi":3078,"resource_url":3079,"first_page":3080,"last_page":3081,"pdf_url":3082,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3083,"paper_type":2659,"authors":3084,"abstract":3090},"lrec2026-ws-dialres-23","Pontic Greek in the Caucasus: an online corpus ","10.63317\u002F5h5zzmh3vmmh","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-23","230","237","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.23.pdf","berikashvili-etal-2026-pontic",[3085,3088],{"paper_id":3076,"author_seq":459,"given_name":3086,"surname":3087,"affiliation":135,"orcid":135},"Svetlana","Berikashvili",{"paper_id":3076,"author_seq":434,"given_name":2019,"surname":3089,"affiliation":135,"orcid":135},"Skopeteas","This paper presents a multi-media corpus of Pontic Greek as spoken by Pontic Greek speakers in the Caucasus (Georgia). The corpus covers three major stages reflecting different sociolinguistic settings: (a) Ponitc Greek in small rural communities in Georgia (original settlements); (b) internal migration to urban centers (within Georgia), (c) external migration (to Greece). The dataset comprises 373 audio recordings (total duration 7h 26m; total word count: 43.073). The open-access resource includes audio files (wav) and annotations (xml). Annotations provide orthographical transcription, morphemic transcription and morpheme-by-morpheme and sentence-by-sentence translations in English (Toolbox); transcriptions are time-aligned with the audio files (ELAN). This collection is intended to linguists working on dialectology and language contact, as well as people with broader interests about the history and practices of this community. Pontic Greek in the Caucasus offers a unique opportunity to investigate contact between Greek and another Indo-European language (Russian) as well as two Non-Indo-European languages (Georgian, Turkish).",{"paper_id":3092,"title":3093,"year":7,"month":358,"day":135,"doi":3094,"resource_url":3095,"first_page":3096,"last_page":3097,"pdf_url":3098,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3099,"paper_type":2659,"authors":3100,"abstract":3119},"lrec2026-ws-dialres-24","Meaning Over Morphology: A Multi-Metric Benchmark of LLMs for Bangla Dialect Translation ","10.63317\u002F3h8y7fs2zbq4","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-24","238","255","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.24.pdf","niloy-etal-2026-meaning",[3101,3104,3107,3110,3113,3116],{"paper_id":3092,"author_seq":459,"given_name":3102,"surname":3103,"affiliation":135,"orcid":135},"Soumik Deb","Niloy",{"paper_id":3092,"author_seq":434,"given_name":3105,"surname":3106,"affiliation":135,"orcid":135},"Subhey Sadi","Rahman",{"paper_id":3092,"author_seq":408,"given_name":3108,"surname":3109,"affiliation":135,"orcid":135},"Mahbub","E Sobhani",{"paper_id":3092,"author_seq":387,"given_name":3111,"surname":3112,"affiliation":135,"orcid":135},"Md. Golam Rabiul","Alam",{"paper_id":3092,"author_seq":358,"given_name":3114,"surname":3115,"affiliation":135,"orcid":135},"Farig Yousuf","Sadeque",{"paper_id":3092,"author_seq":333,"given_name":3117,"surname":3118,"affiliation":135,"orcid":135},"Md. Rezuwan","Hassan","Regional dialects of Bangla, such as Sylheti and Chittagonian, pose significant challenges for natural language processing due to their low-resource nature and substantial linguistic variation from standard Bangla. In this work, we present a systematic evaluation of eight open-source LLMs for translating fifteen distinct Bangla dialects into standard Bangla. To achieve this comprehensive coverage, we utilize a combination of established benchmarks and a novel dataset curated from an ongoing regional linguistic project. We assess model performance using a multi-metric framework that combines exact-match and error-rate evaluations such as, Averaged BLEU, WER, and CER with embedding-based semantic metrics including BERTScore, METEOR, and COMET. Additionally, we perform a detailed dialect-level linguistic analysis to identify the deep-seated structural, orthographic, and semantic barriers inherent to dialectal translation. Our study highlights the strengths and limitations of current open-source models, provides empirical insights for future dialect-aware fine-tuning, and contributes a reproducible benchmark for the research community.",{"paper_id":3121,"title":3122,"year":7,"month":358,"day":135,"doi":3123,"resource_url":3124,"first_page":3125,"last_page":3126,"pdf_url":3127,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3128,"paper_type":2659,"authors":3129,"abstract":3147},"lrec2026-ws-dialres-25","Sociolinguistic aspects of crowdsourcing for a vocal corpus of Alsatian ","10.63317\u002F5ch7cwah438g","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-25","256","264","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.25.pdf","erhart-etal-2026-sociolinguistic",[3130,3133,3135,3138,3141,3144],{"paper_id":3121,"author_seq":459,"given_name":3131,"surname":3132,"affiliation":135,"orcid":135},"Pascale","Erhart",{"paper_id":3121,"author_seq":434,"given_name":3134,"surname":2807,"affiliation":135,"orcid":135},"Lucile",{"paper_id":3121,"author_seq":408,"given_name":3136,"surname":3137,"affiliation":135,"orcid":135},"Sam","Bigeard",{"paper_id":3121,"author_seq":387,"given_name":3139,"surname":3140,"affiliation":135,"orcid":135},"Carole","Werner",{"paper_id":3121,"author_seq":358,"given_name":3142,"surname":3143,"affiliation":135,"orcid":135},"Malek","Yaich",{"paper_id":3121,"author_seq":333,"given_name":3145,"surname":3146,"affiliation":135,"orcid":135},"Slim","Ouni","Alsatian is a regional low-resource language spoken in a majority-language context. In order to create a voice dataset suited for training automatic speech recognition and speech-to-text models, we launched a crowdsourcing campaign on the platform Mozilla Common Voice. We describe sociolinguistic issues we ran into, such as participants’ perception of their own language and its role in the AI landscape, which are vital to address to raise the participation in the crowdsourcing effort. We found that the participants are often confused about NLP and AI tools, and have a strong interested in preserving their language.",{"paper_id":3149,"title":3150,"year":7,"month":358,"day":135,"doi":3151,"resource_url":3152,"first_page":3153,"last_page":3154,"pdf_url":3155,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3156,"paper_type":2659,"authors":3157,"abstract":3166},"lrec2026-ws-dialres-26","HeptaTAX: A Neuro-Symbolic Pipeline and Benchmark for Classifying 16th-Century Heptanesian Notarial Acts ","10.63317\u002F2sh2japrnd73","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-26","265","273","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.26.pdf","chatzikyriakidis-etal-2026-heptatax",[3158,3161,3163],{"paper_id":3149,"author_seq":459,"given_name":3159,"surname":3160,"affiliation":135,"orcid":135},"Stergios","Chatzikyriakidis",{"paper_id":3149,"author_seq":434,"given_name":1717,"surname":3162,"affiliation":135,"orcid":135},"Karantzola",{"paper_id":3149,"author_seq":408,"given_name":3164,"surname":3165,"affiliation":135,"orcid":135},"Vasiliki","Makri","This study originates in the investigation of lexical bundles and formulaic language within sixteenth-century Corfiot notarial documents. The observed functional variation across identical formulaic sequences motivated the development of a document classification framework designed to support the structural interpretation of such language. Given that 16th-century Corfiot notarial acts represent a rich, albeit understudied, dialectal resource, their systematic categorization into subgenres is essential for their full exploration. However, this task requires substantial manual work, while NLP tools for this task and dialect do not exist. In this paper, we attempt to take an initial step in this direction. First, we present a corpus of 1,088 notarial acts from 5 notaries spanning 1500-1567, a 3-tier annotation schema (17 core genres, extension subcategories, hybrid cross-cutting tags), and a 40-act benchmark with gold annotations at all three tiers. Then, we evaluate 12 LLMs across 4 architectures, zero-shot, few-shot, full-context and Neuro-Symbolic. For the latter, we introduce a symbolic engine comprising a set of deterministic rules for identifying discriminative legal formulae, whose output is then injected into the neural (LLM) engine. The results show that the NeSy architecture compresses the accuracy gap between stronger and weaker models from 47.5 pp to 12.5 pp, with the smallest model (Llama 3.1 8B) gaining 47.5% and matching frontier models that operate without symbolic support. Three models reach a ceiling of 72.5% on the core tier. However, consistent errors in procedurally dense material reveal the limits of lexical and formulaic cues for identifying legal effect, motivating the use of symbolic signals in the NeSy pipeline. Extension and hybrid classification remain open challenges, with best scores of ∼63% and ∼35% respectively.",{"paper_id":3168,"title":3169,"year":7,"month":358,"day":135,"doi":3170,"resource_url":3171,"first_page":3172,"last_page":3173,"pdf_url":3174,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3175,"paper_type":2659,"authors":3176,"abstract":3183},"lrec2026-ws-dialres-27","Towards Semantic Access and Interoperability in Digital Dialectal Atlases. A Case Study ","10.63317\u002F2iin2nudtojf","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-27","274","286","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.27.pdf","marongiu-etal-2026-semantic",[3177,3180],{"paper_id":3168,"author_seq":459,"given_name":3178,"surname":3179,"affiliation":135,"orcid":135},"Paola","Marongiu",{"paper_id":3168,"author_seq":434,"given_name":3181,"surname":3182,"affiliation":135,"orcid":135},"Simonetta","Montemagni","The increasing digital availability of dialectal atlases has significantly enhanced access to dialectal data and their potential for linguistic and cultural studies. However, despite their richness, such resources often remain difficult to integrate into contemporary data-driven research workflows, due to complex data structures and limitated interoperability. Most digital dialectal atlases still rely on traditional access models centered on maps, offering only implicit and coarse-grained semantic structures, which limits concept-based exploration and potential for integration with other linguistic resources in the Linguistic Linked Open Data (LLOD) ecosystem. This paper presents a case study carried out on the Atlante Lessicale Toscano (ALT), aimed at addressing these limitations through the introduction of an explicit semantic layer designed to support both user-oriented exploration and machine-actionable interoperability. While ALT already provides a conceptual organization of dialectal materials, this structure was originally conceived for human navigation and not for integration with other computational lexical-semantic resources. To bridge this gap, we align ALT concepts with ItalWordNet, leveraging its synset-based model as a widely adopted semantic backbone in NLP and LLOD infrastructures. The case study focuses on the domain of agriculture, whose historically grounded conceptual distinctions are often underrepresented in general-purpose lexical resources. The paper proposes a mapping strategy, analyzes coverage and mismatch patterns, and releases a new aligned resource mapping ALT agricultural concepts to ItalWordNet, thereby creating the prerequisites for interoperability and reusability of dialectal atlas data.",{"paper_id":3185,"title":3186,"year":7,"month":358,"day":135,"doi":3187,"resource_url":3188,"first_page":3189,"last_page":3190,"pdf_url":3191,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3192,"paper_type":2659,"authors":3193,"abstract":3212},"lrec2026-ws-dialres-28","A CLDF-Compliant Lexical Database for Modern Greek Dialects: Resource Design and Dialectometric Analysis ","10.63317\u002F33w8udv295cp","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-28","287","299","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.28.pdf","bompolas-etal-2026-cldf",[3194,3195,3198,3201,3204,3207,3209],{"paper_id":3185,"author_seq":459,"given_name":2019,"surname":2020,"affiliation":135,"orcid":135},{"paper_id":3185,"author_seq":434,"given_name":3196,"surname":3197,"affiliation":135,"orcid":135},"Natalia","Chousou-Polydouri",{"paper_id":3185,"author_seq":408,"given_name":3199,"surname":3200,"affiliation":135,"orcid":135},"Manuela","Genitsaridi",{"paper_id":3185,"author_seq":387,"given_name":3202,"surname":3203,"affiliation":135,"orcid":135},"Danae","Karatzanou",{"paper_id":3185,"author_seq":358,"given_name":3205,"surname":3206,"affiliation":135,"orcid":135},"Georgios","Kostopoulos",{"paper_id":3185,"author_seq":333,"given_name":1405,"surname":3208,"affiliation":135,"orcid":135},"Anagnostopoulou",{"paper_id":3185,"author_seq":309,"given_name":3210,"surname":3211,"affiliation":135,"orcid":135},"Dimitra","Melissaropoulou","This paper presents the first CLDF database that systematically documents lexical variation across 36 Modern Greek varieties (including Standard Modern Greek). The dataset aligns 14,378 lexical items over 345 concepts, links varieties to stable identifiers (Glottocodes), introduces a Greek-specific concept list, and maps meanings to standardized Concepticon concept sets, enabling interoperability and reproducible workflows. To assess whether the database preserves a meaningful dialectological signal, we conduct a dialectometric analysis by computing feature-sensitive string distances over IPA transcriptions and applying hierarchical clustering. The resulting similarity structure recovers major macro-divisions—most notably a broad Northern vs. Southern partition among Koine-descended mainland varieties—and isolates peripheral groups with distinct historical trajectories (e.g., Asia Minor, Italiot, Tsakonian). The database provides scalable infrastructure for quantitative dialectology, comparative Greek linguistics, and dialect-aware language technology.",{"paper_id":3214,"title":3215,"year":7,"month":358,"day":135,"doi":3216,"resource_url":3217,"first_page":3218,"last_page":3219,"pdf_url":3220,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3221,"paper_type":2659,"authors":3222,"abstract":3232},"lrec2026-ws-dialres-29","A Speech Resource for the Pontic Greek Dialect: Transcription Choices and Baseline ASR Evaluation ","10.63317\u002F3rq2murx2626","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-29","300","307","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.29.pdf","konstantinidou-etal-2026-speech",[3223,3226,3229,3230,3231],{"paper_id":3214,"author_seq":459,"given_name":3224,"surname":3225,"affiliation":135,"orcid":135},"Rodanna","Konstantinidou",{"paper_id":3214,"author_seq":434,"given_name":3227,"surname":3228,"affiliation":135,"orcid":135},"Chara","Tsoukala",{"paper_id":3214,"author_seq":408,"given_name":2022,"surname":2023,"affiliation":135,"orcid":135},{"paper_id":3214,"author_seq":387,"given_name":1475,"surname":1476,"affiliation":135,"orcid":135},{"paper_id":3214,"author_seq":358,"given_name":427,"surname":428,"affiliation":135,"orcid":135},"Pontic Greek is a living but endangered Modern Greek dialect that lacks publicly available AI-oriented speech resources and ASR benchmarks. This work reports on the first systematic inference-only (zero-shot) ASR evaluation on authentic Pontic speech. Progress on Pontic ASR is hindered by two coupled challenges: the scarcity of transcribed speech data and the absence of a standardized orthography, which makes it difficult to create consistent reference transcriptions for evaluation. We address these challenges by releasing a new speech corpus of contemporary Pontic as spoken in Northern Greece, derived from natural conversations and provided with manual, utterance-level, time-aligned transcriptions. To reduce annotator bias and increase practical usability, we collect community evidence on written-form preferences via a small questionnaire and use the observed patterns to guide a consistent Greek-script transcription scheme. We use this corpus to perform inference-only (zero-shot) ASR evaluation, benchmarking four state-of-the-art pretrained speech recognition models under a unified evaluation protocol. Results show that zero-shot recognition remains challenging, establishing baseline figures and underscoring the need for dialect-specific data and adaptation.",{"paper_id":3234,"title":3235,"year":7,"month":358,"day":135,"doi":3236,"resource_url":3237,"first_page":3238,"last_page":3239,"pdf_url":3240,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3241,"paper_type":2659,"authors":3242,"abstract":3257},"lrec2026-ws-dialres-30","First Steps in ASR for Cypriot Greek: Challenges and Insights ","10.63317\u002F2iznmge7hdtx","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-30","308","314","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.30.pdf","stamou-etal-2026-first",[3243,3244,3247,3250,3252,3255],{"paper_id":3234,"author_seq":459,"given_name":2022,"surname":2023,"affiliation":135,"orcid":135},{"paper_id":3234,"author_seq":434,"given_name":3245,"surname":3246,"affiliation":135,"orcid":135},"Spyros","Armostis",{"paper_id":3234,"author_seq":408,"given_name":3248,"surname":3249,"affiliation":135,"orcid":135},"Antigoni","Klimi",{"paper_id":3234,"author_seq":387,"given_name":3205,"surname":3251,"affiliation":135,"orcid":135},"Paraskevopoulos",{"paper_id":3234,"author_seq":358,"given_name":3253,"surname":3254,"affiliation":135,"orcid":135},"Vassilis","Katsouros",{"paper_id":3234,"author_seq":333,"given_name":3256,"surname":2011,"affiliation":135,"orcid":135},"Antonios","This paper presents the first automatic speech recognition (ASR) system for Cypriot Greek, a non-standardized variety of Modern Greek with distinctive phonological, lexical, and orthographic characteristics. We adapt Whisper, a state-of-the-art multilingual ASR model, to Cypriot Greek through fine-tuning on the mozilla common voice spontaneous speech dataset for Cypriot Greek. The phonological and lexical divergence of Cypriot Greek from Standard Modern Greek poses significant challenges for mainstream ASR, particularly under conditions of limited training data and dialectal variation. Results demonstrate that whisper-medium achieved a best word error rate (WER) of 37.85%, while whisper-large-v3 consistently outperformed it, reaching a minimum WER of 33.93%. In the light of these findings, increased model size, combined with targeted fine-tuning on normalized dialectical data, significantly improves recognition accuracy, indicating that careful handling of orthographic and dialectical variation provides an effective path for ASR adaptation to low-resource varieties.",{"paper_id":3259,"title":3260,"year":7,"month":358,"day":135,"doi":3261,"resource_url":3262,"first_page":3263,"last_page":3264,"pdf_url":3265,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3266,"paper_type":2659,"authors":3267,"abstract":3278},"lrec2026-ws-dialres-31","Structural Divergence under Shared Language-Level Specification: Griko in Universal Dependencies ","10.63317\u002F3qewtwwnj3cx","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-31","315","326","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.31.pdf","bompolas-etal-2026-structural",[3268,3269,3271,3274,3277],{"paper_id":3259,"author_seq":459,"given_name":2019,"surname":2020,"affiliation":135,"orcid":135},{"paper_id":3259,"author_seq":434,"given_name":2766,"surname":3270,"affiliation":135,"orcid":135},"Pinna",{"paper_id":3259,"author_seq":408,"given_name":3272,"surname":3273,"affiliation":135,"orcid":135},"Josep","Quer",{"paper_id":3259,"author_seq":387,"given_name":3275,"surname":3276,"affiliation":135,"orcid":135},"Marika","Lekakou",{"paper_id":3259,"author_seq":358,"given_name":427,"surname":428,"affiliation":135,"orcid":135},"Dialectal varieties pose major challenges for NLP resource development, especially when annotation frameworks are organized around standardized language specifications. In Universal Dependencies (UD), dialects without independent ISO codes are subsumed under the corresponding standard language and inherit its language-level documentation, validator settings, and grammatical inventories. This paper examines Griko, a Greek variety spoken in southern Italy that developed in relative isolation from the Modern Greek dialect continuum while remaining in long-term contact with local Italo-Romance varieties. We assess the consequences of this organizational structure through controlled parsing experiments comparing intra-dialectal training, cross-dialectal transfer from Standard Modern Greek (SMG), script-controlled transfer using romanized SMG, and contact-related cross-lingual transfer from Italian. Our results show that, before romanization, the Italian model even surpasses SMG on several UD metrics and that, although romanization substantially improves SMG-based transfer, performance still remains far below the intra-dialectal baseline. We argue that this persistent gap reflects the interaction between structural divergence and language-level validation constraints, a phenomenon we term ISO-based validation coupling. Through analyses of auxiliary systems, voice marking, and progressive constructions, we show how standard-centric validation architectures can constrain the representation of dialect-specific grammar. More broadly, the Griko case highlights the limitations of language-centric organization in UD and underscores the need for variety-sensitive mechanisms when extending universal annotation frameworks to structurally divergent dialects.",{"paper_id":3280,"title":3281,"year":7,"month":358,"day":135,"doi":3282,"resource_url":3283,"first_page":3284,"last_page":3285,"pdf_url":3286,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3287,"paper_type":2659,"authors":3288,"abstract":3295},"lrec2026-ws-dialres-32","Digital Preservation of Aromanian Through Knowledge Management and Automatic Speech Recognition Evaluation ","10.63317\u002F34e4awcc69g4","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-32","327","336","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.32.pdf","pendevska-etal-2026-digital",[3289,3292],{"paper_id":3280,"author_seq":459,"given_name":3290,"surname":3291,"affiliation":135,"orcid":135},"Marija","Pendevska",{"paper_id":3280,"author_seq":434,"given_name":3293,"surname":3294,"affiliation":135,"orcid":135},"Hristina","Nastevska","This paper presents a knowledge management framework for the digital preservation of Aromanian, an endangered Eastern Romance language spoken across the Balkans, combined with the first systematic evaluation of automatic speech recognition (ASR) models on Aromanian dialectal speech. The proposed three-module framework encompasses localization of existing resources, distribution through digital platforms, and creation of new linguistic content through technological innovation. Empirical analysis of knowledge management practices among 176 respondents validates the framework design, revealing that information quality (28.3%) and personal utility (24.1%) are the most valued criteria for knowledge sharing, while digital information seeking (27.1%) is the dominant behaviour. Within this framework, we evaluate models from the OpenAI Whisper family across three sizes (medium, large-v2, large-v3) and multiple language settings on two Aromanian varieties: Gramosteanj and Crushova. All configurations yield word error rates (WER) above 88%, with character error rates (CER) as low as 34% under optimal conditions, indicating partial phonotactic capture despite word-level failure. The Latin language setting with large-v3 consistently achieves the best results. Romanization of non-Latin script output substantially reduces CER, confirming script mismatch as a major error source. These findings underscore the limitations of current pretrained ASR models for endangered languages and the need for dedicated resources and adaptation strategies within a broader language preservation framework",{"paper_id":3297,"title":3298,"year":7,"month":358,"day":135,"doi":3299,"resource_url":3300,"first_page":3301,"last_page":3302,"pdf_url":3303,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3304,"paper_type":2659,"authors":3305,"abstract":3312},"lrec2026-ws-dialres-33","A Novel Typology of Mutually Intelligible Words: The Case of Slavic Languages ","10.63317\u002F3vbs5uvmkggc","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-33","337","344","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.33.pdf","klyshinsky-etal-2026-novel",[3306,3309],{"paper_id":3297,"author_seq":459,"given_name":3307,"surname":3308,"affiliation":135,"orcid":135},"Edward","Klyshinsky",{"paper_id":3297,"author_seq":434,"given_name":3310,"surname":3311,"affiliation":135,"orcid":135},"Yulia","Badryzlova","In this paper, we demonstrate that using the notion of cognate in the task of evaluating mutual intelligibility (MI) in closely related languages can be confusing. We suggest a new term – percipiants – which handles MI of words irrespective of whether they have a common origin. We propose four classes of percipiants,which are differentiated by the degree and type of the closeness of meanings within the word pair. Furthermore, we claim that MI of individual words across a set of languages may be established computationally via normalized Levenshtein Distance (LD). We verify our hypotheses by analyzing data from a psycholinguistic experiment where the respondents were to predict words missing in a text in their native language. In the experimental condition, the respondents had access to the text in their native language and in a language from the same language group (Slavic); in the control group, the respondents were performing the same test in the presence of their native language only. The analysis demonstrates that (a) psycholinguistically, MI may be defined as the difference between the average correctness of answers in the experimental and the control groups; (b) normalized LD may serve as an adequate predictor of the experimentally measured MI; (c) this MI corroborates the four classes of percipiants; (d) contextual factors weaken the predictive force of LD, requiring further investigation.",{"paper_id":3314,"title":3315,"year":7,"month":358,"day":135,"doi":3316,"resource_url":3317,"first_page":3318,"last_page":3319,"pdf_url":3320,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3321,"paper_type":2659,"authors":3322,"abstract":3326},"lrec2026-ws-dialres-34","Transfer Learning for an Endangered Slavic Variety: Dependency Parsing in Pomak Across Contact-Shaped Dialects ","10.63317\u002F2mrmccuuhkgf","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-dialres-34","345","354","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fdialres\u002Fpdf\u002F2026.dialres-1.34.pdf","karakas-2026-transfer",[3323],{"paper_id":3314,"author_seq":459,"given_name":3324,"surname":3325,"affiliation":135,"orcid":135},"Sercan","Karakas","This paper presents new resources and baselines for Dependency Parsing in Pomak, an endangered Eastern South Slavic language with substantial dialectal variation and no widely adopted standard. We focus on the variety spoken in Turkey (Uzunköprü) and ask how well a dependency parser trained on the existing Pomak Universal Dependencies treebank, which was built primarily from the variety that is spoken in Greece, transfers across dialects. We run two experimental phases. First, we train a parser on the Greek-variety UD data and evaluate zero-shot transfer to Turkish-variety Pomak, quantifying the impact of phonological and morphosyntactic differences. Second, we introduce a new manually annotated Turkish-variety Pomak corpus of 650 sentences and show that, despite its small size, targeted fine-tuning substantially improves accuracy; performance is further boosted by cross-variety transfer learning that combines the two dialects."]