# 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.

Source: https://aibyos.com/research/asr

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-time**streaming on one GPU

**in-region**audio 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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## Typical tooling

-   Whisper
-   NVIDIA NeMo
-   CTranslate2
-   Label Studio

## Related

[All research](https://aibyos.com/research)[Sovereign GPU compute](https://aibyos.com/gpu)[Fine-tuning](https://aibyos.com/services/fine-tuning)