Resources
How to evaluate low-resource language transcripts
Build a small, reviewed reference set, choose explicit text conventions, and inspect errors by declared language and recording condition before expanding a speech dataset.
Published 2026-10-28 · 5 min read
Build a small, reviewed reference set, choose explicit text conventions, and inspect errors by declared language and recording condition before expanding a speech dataset.
Use the linked free tool to work through the practical steps. Keep the original files and documentation unchanged, and save your report with the date and scope of the review.
Define the language and transcription convention
Agree which language varieties, spelling systems, scripts and code-switching patterns belong in scope. A model transcript and reference can express the same utterance with different spellings. Decide which variations count as errors and document that decision before the evaluation.
Create reference transcripts with reviewers who understand the language and the task. Keep reviewer disagreements and unresolved spans visible. Do not treat a model-generated transcript as verified ground truth simply because it appears fluent.
Use CER with a documented character policy
If whitespace does not reliably mark words, a whitespace-based WER can be misleading. CER avoids that particular tokenization issue, but still depends on Unicode normalization, whitespace treatment and the transcription convention. It does not automatically solve spelling variation or semantic equivalence.
The free workbench uses NFC and Unicode code points. It does not segment grapheme clusters, transliterate scripts, normalize dialects or perform morphological analysis. Record those boundaries when reporting scores, and use specialist language tooling if the task requires a different policy.
Prepare aligned utterance pairs
Give each reference and hypothesis the same stable utterance identifier. For batch evaluation, provide one JSON object per line containing id, reference and hypothesis. Keep source recordings separate and preserve the mapping in your private evaluation package.
Compare the same audio intervals. Different segmentation can introduce apparent insertions and deletions that are really alignment problems. Check a sample manually before calculating corpus results. Fiund rejects duplicate IDs so records are not silently merged, and limits batch size to keep local processing manageable.
Turn the error analysis into a collection brief
Inspect errors across declared language varieties, environments, speakers and task conditions using metadata you have permission to maintain. Avoid inferring sensitive speaker characteristics from voice. A single aggregate number can conceal a weak slice of the collection.
Use recurring errors to define the next pilot: for example, more examples of a specific vocabulary or acoustic condition, with explicit permissions and acceptance tests. Share requirements through a buyer brief or describe collector capabilities through the collector intake. A tool score is evidence for a decision, not a guarantee that more hours alone will fix a model.