Langigs Insights
Practical ideas for better language and AI data.
Guides and practical articles on African and Asian language technology, speech and annotation data, localization and multilingual AI.
Tshivenda (Venda) Language Data for AI: What Project Teams Should Know
A practical introduction to scoping Tshivenda language data projects, including regional variety, orthography, speaker context, consent, and evaluation needs.
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Bengali Language Data for AI: Speech, Text, and Annotation Considerations
How to plan Bengali language datasets with attention to script, regional variation, domain, recording context, and task-specific quality checks.
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Hausa Speech Data and Transcription: Planning for Real-World AI Use
Key choices for Hausa speech collection, transcription conventions, dialect coverage, consent, and quality review.
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Yoruba NLP and Language Data: Tone, Orthography, and Context
Why tone marking, spelling conventions, context, and reviewer guidance matter when preparing Yoruba data for AI.
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Igbo Language Data for AI: Dataset Design and Quality Questions
A task-first checklist for planning Igbo text, speech, transcription, and annotation projects without overlooking orthographic variation.
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Swahili Speech and Text Datasets: Choosing the Right Coverage
How regional variety, task design, domain coverage, and consent shape useful Swahili datasets for multilingual AI.
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Amharic Language Data for AI: Script, Speech, and Evaluation
Project considerations for Amharic data, including Ethiopic script handling, audio coverage, normalization, and reviewer guidance.
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Nepali Language Data for AI: Text, Speech, and Regional Context
How to specify Nepali text and speech datasets with attention to Devanagari, normalization, regional coverage, and task fit.
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Wolof Language Technology: Scoping Data for Speech and Text Tasks
A practical framework for discussing Wolof speech, text, and annotation needs while documenting language variety and data limitations.
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Nigerian Pidgin AI Data: Capturing Everyday Speech and Meaning
Why Nigerian Pidgin projects should account for code-switching, conversational context, transcription choices, and diverse speakers.
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