Research · Voice & speech

Speech recognition for a low-resource language

Fine-tune a Whisper-class model for a language, accent or domain that general models handle poorly, e.g. Azerbaijani, Polish dialects or medical dictation.

30–50%relative drop in word error rate
real-timestreaming on one GPU
in-regionaudio never leaves your jurisdiction

Pipeline

  1. Audit and clean in-domain audio and transcripts
  2. Augment with synthetic speech and noise
  3. Fine-tune and evaluate on word/character error rate
  4. Add custom vocabulary and punctuation models
  5. Deploy streaming inference with latency monitoring

Figures show the typical order of magnitude for this approach compared with calling a large general-purpose model. Actual results depend on the task and data; we measure them on your data during the baseline phase.

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