--- title: "2026 Dataset Vote Results | Way With Words AI" description: "Thank you to everyone who voted and shared our 2026 community dataset campaign. See final results and what we are building next." image: "https://waywithwords.ai/og-default.png" --- 2026 community vote, concluded # Thank you for shaping our roadmap Voting for our first community dataset campaign closed on 15 May 2026. Thank you to everyone who cast a vote, and to everyone who shared the page with colleagues, students, and partners. Your input directly guides what we build next. ### What happens next We are now reviewing feasibility and production paths for the top-voted datasets, starting with the community's first priority below. We will share updates as projects move forward. * Assessing technical scope, ethics, and resourcing for the highest-ranked proposals. * Engaging partners where there is strong institutional interest or funding alignment. * Publishing progress on this page and through our usual channels as work advances. If your organisation can contribute time, expertise, or funding toward a listed dataset, [we would love to hear from you](/contact). Final results 20 votes total Community priorities from our 2026 vote. We are exploring how to make these datasets a reality. #1 community priority Camfranglais Conversational Speech and Annotation Dataset (CCSAD) Camfranglais 5 votes #2 community priority Multilingual Multi-Speaker Conversational Corpus ENG, AFR, ZUL, SOT, XHO 4 votes #3 community priority Code-Switching Conversational Corpus SWA, YOR, HAU 4 votes [View final results](#proposed-collections) [Suggest a dataset](#suggest-dataset) Voting concluded · Final results below Language coverage See the interactive [Languages across Africa](/african-languages) map for countries and dataset coverage for languages in each zone. [Open interactive map](/african-languages) ## Proposed collections Final vote counts from our 2026 campaign (20 votes total). Sorted by community support. Showing 1-7 of 7 dataset options. Option 1 Conversational Speech (Code-switching + Annotation) #### Camfranglais Conversational Speech and Annotation Dataset (CCSAD) Community votes 5 votes (25%) Structured Camfranglais conversational speech dataset capturing natural code-switching, lexical innovation, and context-dependent meaning in real usage contexts. Type Conversational Speech (Code-switching + Annotation) Domain Urban Conversations, Sociolinguistics, Digital Humanities Languages Camfranglais Deliverables Recordings + Transcripts Objective: Build a research-oriented, ethically collected Camfranglais corpus that documents naturally occurring hybrid speech with layered annotations for linguistic research, preservation, and future low-resource language technology work. View technical specification Option 2 Conversational Speech (Multi-speaker) #### Code-Switching Conversational Corpus Community votes 4 votes (20%) Multi-speaker conversational dataset focused on natural code-switching across Swahili, Yoruba, and Hausa for robust multilingual dialogue modeling. Type Conversational Speech (Multi-speaker) Domain Customer Support, Healthcare, Agriculture Languages SWA, YOR, HAU Deliverables Recordings + Transcripts Objective: Build a high-quality conversational corpus that captures realistic turn-taking and intra-utterance code-switching patterns across three widely used African languages. View technical specification Option 3 Conversational Speech (Multi-speaker) #### Multilingual Multi-Speaker Conversational Corpus Community votes 4 votes (20%) Multi-speaker conversational recordings designed for realistic dialogue behaviour, sentiment analysis, and robust multilingual speech modeling with code-switching. Type Conversational Speech (Multi-speaker) Domain Customer Support, FinTech, Telecom Languages ENG, AFR, ZUL, SOT, XHO Deliverables Recordings + Transcripts Objective: Create a high-coverage multilingual conversational dataset with natural turn-taking and domain-relevant interactions for speech and language model training. View technical specification Option 4 Parallel Speech (Scripted + Translated) #### Parallel Multilingual Speech Translation Corpus (Healthcare) Community votes 3 votes (15%) Parallel healthcare speech corpus with aligned multilingual scripts to improve ASR, speech translation, and cross-lingual instruction fidelity. Type Parallel Speech (Scripted + Translated) Domain Healthcare Languages ENG, AFR, ZUL, SOT, XHO Deliverables Recordings + Transcripts + Translations Objective: Create a high-quality parallel speech dataset with semantic alignment across 5 languages for multilingual, voice-to-voice healthcare communication tasks. View technical specification Option 5 Multimodal (Image + Speech Prompting) #### Multilingual Multimodal South African Culture Corpus Community votes 2 votes (10%) Rights-cleared cultural image corpus paired with multilingual spoken prompts to support vision-language grounding in underrepresented local contexts. Type Multimodal (Image + Speech Prompting) Domain South African culture Languages ENG, AFR, ZUL, SOT, XHO Deliverables Recordings + Transcripts + Images Objective: Build a legally compliant multimodal benchmark where each image is paired with language-specific spoken prompts for model training and evaluation. View technical specification Option 6 Conversational Speech (Multi-speaker) #### Underrepresented SA Languages Conversational Corpus Community votes 2 votes (10%) Multi-speaker conversational dataset focused on underrepresented South African languages with natural code-switching patterns for robust multilingual speech modeling. Type Conversational Speech (Multi-speaker) Domain Customer Support, FinTech, Education Languages TSN, NSO, SSW, VEN, NBL, TSO Deliverables Recordings + Transcripts Objective: Build a high-quality conversational corpus that captures natural turn-taking and code-switching behaviour across six underrepresented South African languages. View technical specification Option 7 LLM Q&A (Instructional / How-to) #### Everyday Language Instruction Benchmark (Q&A) Community votes 0 votes (0%) A culturally grounded Shona instruction and Q&A dataset for practical everyday guidance, designed to support localised LLM training and evaluation. Type LLM Q&A (Instructional / How-to) Domain Daily Life, Home and Family, Education, Health Access, Micro-business Languages Shona Deliverables Validated Q&A Pairs Objective: Build a domain-balanced Shona instruction and Q&A resource for practical everyday guidance, with approximately 1,500 validated pairs written in locally natural phrasing to support localised LLM evaluation and fine-tuning. View technical specification ### Have another dataset idea? Suggest a new dataset for internal review. You can choose multiple languages and dataset types. Suggest a dataset Suggestions are reviewed by the Way With Words team for feasibility, impact, and potential funding alignment before being added as a voting option. Website Name Surname Email Name of company or learning institution Suggested dataset title Proposed domain(s) (optional) Minimum number of hours to record (optional) What is your use case for this dataset? Would you contribute funding or help find funding for this dataset? Select one Yes, we can contribute funding Yes, we can contribute time / expertise Yes, we can help find funding partners Yes, both Not at this stage African language(s) (select one or more) Afrikaans Amharic Arabic (North Africa) Berber (Tamazight) Chichewa English (South African) Fang Fulfulde Hausa Igbo isiNdebele isiXhosa isiZulu Kituba Kinyarwanda Lingala Luganda Oromo Sango seSotho seTswana Shona Somali Swahili Tigrinya Tshiluba Tshivenḓa Xitsonga Yoruba Dataset type(s) (select one or more) Multi-modal Text LLM Q&A (Instructional / How-to) Voice Parallel languages Multi-speaker Monolingual Bilingual Domain-specific Conversational Read speech Additional notes (optional) By submitting, you agree that Way With Words may use the information you provide to review your dataset suggestion and understand language and dataset demand. See our [Privacy Notice](/privacy). Send suggestion Technical spec Close Languages Deliverables Primary ML use cases Quality and evaluation focus [Talk to us about your dataset idea](/contact#contact-form) ```json {"@context":"https://schema.org","@type":"Organization","name":"Way With Words AI","url":"https://waywithwords.ai","email":"hello@waywithwords.ai","contactPoint":[{"@type":"ContactPoint","contactType":"customer support","telephone":"+44 208 157 9929","email":"hello@waywithwords.ai","areaServed":"GB","availableLanguage":"en"},{"@type":"ContactPoint","contactType":"customer support","telephone":"+27 21 879 3552","email":"hello@waywithwords.ai","areaServed":"ZA","availableLanguage":"en"}],"location":[{"@type":"Place","name":"Way With Words Limited (UK Office)","address":{"@type":"PostalAddress","streetAddress":"Caledonian House Business Centre, 164 High Street","addressLocality":"Elgin","postalCode":"IV30 1BD","addressCountry":"GB"}},{"@type":"Place","name":"Way With Words SA (Pty) Ltd (South Africa & SADC Office)","address":{"@type":"PostalAddress","streetAddress":"First Floor, Vineyards Square North, The Vineyards Office Estate, 99 Jip de Jager Drive, Bellville","addressLocality":"Cape Town","postalCode":"7530","addressCountry":"ZA"}}]} ```