Nesdia Labs
Accelerating Linguistic Research
Traditional Approach
(Years)
Our Approach
(Months)
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Our Technical Approach
Nesdia Labs develops fine-tuning pipelines for multilingual transformer models (mBERT, XLM-RoBERTa) adapted to endangered language data. We employ techniques including morphology-aware subword tokenization, adapter layers for language-specific features, and careful hyperparameter tuning for extremely small corpora.
We process field recordings using automatic speech recognition (ASR) systems, starting with multilingual models (Whisper, wav2vec 2.0) and fine-tuning where transcribed data permits. Output transcriptions undergo human verification. For languages with no existing ASR, we provide manual transcription workflows with time-alignment tools.
Fine-tuning diverges from standard practice when working with corpora of hundreds rather than millions of examples. We employ aggressive regularization, cross-lingual transfer from related high-resource languages, and data augmentation through back-translation where parallel text exists. Results are evaluated against held-out test sets with expert review of error patterns.
Research integrates phonetic annotation (using Praat, ELAN, or equivalent tools) with morphological parsing to create richly annotated corpora. Acoustic features and grammatical structure are linked at the word and morpheme level, enabling phonology-morphology interface studies.
Morphological analysis for under-documented Semitic languages (root-pattern extraction), lexical semantic change detection in historical Icelandic texts, OCR correction pipelines for degraded manuscript images, and ASR adaptation for tonal languages with limited training data.
Cultural Loss
“Linguistic diversity increasingly faces threats as languages disappear, endangering entire cultures and knowledge systems.”
Irreparable Loss
“Every vanishing language represents lost traditional knowledge and cultural heritage requiring prevention.”
Loss For Humanity
“Indigenous languages containing complex knowledge systems face extinction globally, representing identity and heritage loss.”
