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Whisper’s transcripts can be smoother than the speech they represent: in a 2025 benchmark, the models often omitted hesitations and false starts, and sometimes repeated phrases that were not spoken. Across the tested files, average word error rates (WERs) were higher for ICE Nigeria than ICE Scotland for every model—but the study’s fitted model did not find a statistically significant overall corpus effect. The results describe a selected set of recordings, not every Nigerian English speaker or any one transcript.
What the study measured
In “Transcribing Diverse Voices: Using Whisper for ICE corpora,” presented at Interspeech 2025 (17–21 August 2025), Andreas Weilinghoff evaluated six Whisper model versions on recordings from ICE Nigeria and ICE Scotland, two components of the International Corpus of English. The comparison was between Whisper’s plain-text output and the corpora’s manual reference transcripts.
The sample contained 60 sound files from each corpus, drawn from 12 categories shared by both datasets. Those included broadcast news, discussions, lessons, cross-examinations, parliamentary debates, and unscripted speeches. In all, the benchmark covered 120 files, about 24 hours and 56 minutes of audio, and 205,917 reference words.
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| Corpus | Audio in sample | Reference words | Files |
|---|---|---|---|
| ICE Nigeria | 13:05:47 | 94,499 | 60 |
| ICE Scotland | 11:50:31 | 111,418 | 60 |
The author ran the selected recordings through tiny, base, small, medium, large v2, and large v3. The outputs had no timestamps or speaker labels. Using the Python library werpy to normalize and align output with reference text, the study calculated WER for each file, then reported averages and standard deviations and fitted linear mixed-effects models.
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How the model averages compared
The table gives the study’s mean WER for each corpus and model. Parentheses contain the standard deviation across files. WER is conventionally a fraction; multiplying by 100 expresses it as a percentage.
| Whisper model | ICE Nigeria mean WER (SD) | ICE Scotland mean WER (SD) |
|---|---|---|
| tiny | 0.54 (0.30) | 0.32 (0.28) |
| base | 0.45 (0.30) | 0.29 (0.27) |
| small | 0.36 (0.26) | 0.27 (0.27) |
| medium | 0.33 (0.25) | 0.26 (0.27) |
| large v2 | 0.30 (0.24) | 0.26 (0.27) |
| large v3 | 0.29 (0.24) | 0.26 (0.26) |
For example, the Nigeria mean of 0.29 for large v3 is a WER of 29 on a 100-point scale, not necessarily 29 distinct words out of every 100 that are wrong. WER counts substitutions, deletions, and insertions relative to a reference. A word may be involved in more than one edit, so WER can exceed 1.0 (or 100%). The study reports that some individual files did exceed 1.0.
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Across these averages, larger models generally had lower WER, and the Nigeria–Scotland gap narrowed with model size. That is a descriptive pattern in this sample, not a guarantee that upgrading will produce the same change on other recordings.
Why “correcting” can make a transcript less faithful
The study uses “corrects” to describe a mismatch between the spoken material and Whisper’s output, not a demonstrated improvement to the recording. Weilinghoff reports that hesitations and false starts were often omitted, producing a more idealized transcript than the sociolinguistic reference. A transcript can therefore read more smoothly while preserving less of what the speaker actually said.
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The paper also describes repeated hallucinated phrases, particularly in conditions involving poor audio quality, long silences, overlapping speakers, interruptions, or switches to Nigerian Pidgin English. Nigerian English and Nigerian Pidgin are not interchangeable: the benchmark concerns ICE Nigeria recordings, and the Pidgin switches are noted as one difficult condition rather than the study’s sole target variety. Speaker attribution and timestamps may also require correction; they were not part of the plain-text comparison.
What the averages do—and do not—say about Nigerian English
All six descriptive mean WERs were higher for ICE Nigeria than for ICE Scotland. But the mixed-effects analysis considered more than corpus alone, including category, model, recording quality, speaker count, gender grouping, and file duration, with sound file treated as a random factor. In the best reported model, marginal R² was 0.65 and conditional R² was 0.96. The overall corpus effect did not reach statistical significance.
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Those findings are not contradictory. The raw averages show a difference within this selected set; the model’s corpus effect, after accounting for the included factors and file-level variation, was not statistically significant. Neither result establishes an across-the-board ranking of varieties, nor does WER measure intelligibility, usefulness, or the quality of every individual transcript.
Other factors had meaningful associations in the fitted analysis: good-quality audio was associated with a 25% WER decrease, while poly-speaker recordings had roughly 28% higher WER than monologues. Speech category also mattered. These are modelled associations in this benchmark, not promises of a fixed improvement or penalty for a particular recording.
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How to use this evidence when checking a transcript
The benchmark is most useful as a warning against judging a transcription system from one polished output or one average. For quote-sensitive work, linguistic analysis, or any task where every spoken word matters, compare output with audio and decide in advance whether the transcript should preserve disfluencies, interruptions, and speaker turns.
- Check difficult passages against the audio, especially where speakers overlap, pause for a long time, interrupt one another, or switch language or variety.
- Listen for omissions as well as obvious substitutions: a fluent-looking sentence can hide removed hesitations or false starts.
- Inspect repeated phrases and speaker labels rather than assuming that readable text is accurate diarization.
- Evaluate on recordings like the ones you actually need to transcribe. A corpus-wide mean cannot predict a particular file’s WER.
Weilinghoff’s benchmark reflects six model versions available for that 2025 study and a particular selection of ICE recordings. It does not establish how a newer model or a hosted transcription service would perform on the same files. OpenAI’s current API documentation recommends gpt-transcribe for general recorded speech, while Whisper remains relevant for uses such as word- or segment-level timestamps; that documentation context is separate from, and not a rerun of, this benchmark.
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