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What the study measured
A matched sample of two English corpora
Weilinghoff selected 60 sound files from ICE Nigeria and 60 from ICE Scotland, covering 12 categories shared by both corpora. These included broadcast news, discussions, lessons, cross-examinations, parliamentary debates and unscripted speeches. The 120 files totaled about 24 hours, 56 minutes and 18 seconds of audio and contained 205,917 words in the manual reference transcripts.
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| Corpus | Files | Audio | Reference words |
|---|---|---|---|
| ICE Nigeria | 60 | 13:05:47 | 94,499 |
| ICE Scotland | 60 | 11:50:31 | 111,418 |
The figures describe the selected study sample, not the full ICE corpora or a representative survey of all speakers, accents, languages or speaking situations in either country.
Six Whisper versions, scored against manual transcripts
The study retranscribed each recording with six Whisper variants then available: tiny, base, small, medium, large v2 and large v3. The outputs were plain text, without timestamps or speaker labels. The author compared them with the ICE manual transcripts using the Python library werpy, which normalizes and aligns text, then calculated WER for each file.
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WER counts substitutions, deletions and insertions relative to the reference. It is a fraction that can also be expressed as a percentage by multiplying by 100, but it is not simply the share of words that are “wrong”: insertions and multiple edit operations affect the score, and WER can exceed 1.0.
How the model results compare
The table shows the study’s mean WER for each corpus and model. Standard deviations are in parentheses; they indicate substantial spread among files, not uncertainty bounds for an individual transcript.
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| Whisper version | 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) |
Within this sample, the mean WER was higher for ICE Nigeria for all six versions. Moving to larger models generally lowered mean WER, and the descriptive difference between the two corpora narrowed: it was 0.22 for tiny and 0.03 for large v3. These are comparisons of averages on this dataset, not guarantees about which model will do better on a particular file.
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The term refers to a difference between the recording and the transcript, not to proof that Whisper identifies and repairs Nigerian English. Weilinghoff reports that the system sometimes idealized utterances by leaving out audible hesitations and false starts that were retained in the sociolinguistic reference transcripts. A smoother sentence can therefore score worse when the task is to preserve exactly what a speaker said.
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The paper also describes hallucinated repetitions: phrases repeated in the output without corresponding speech. They were observed particularly in difficult audio conditions, including poor recording quality, long silences, overlapping speech, interruptions and switches to Nigerian Pidgin English. Nigerian English and Nigerian Pidgin are not interchangeable labels; the study examined recordings in the ICE Nigeria corpus and mentions Pidgin switches as one challenging condition.
Because the tested outputs had no timestamps or speaker labels, the benchmark’s WER does not assess timestamp accuracy or diarization. Those can require separate checking when a transcript needs to identify who spoke and when.
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Why a corpus average cannot predict one recording
Weilinghoff supplemented the descriptive averages with linear mixed-effects modelling. The analysis considered corpus, text category, model, recording quality, speaker count, gender grouping and file duration, while treating sound file as a random factor. The best reported model had marginal R² of 0.65 and conditional R² of 0.96; these describe model fit, not transcription accuracy.
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- Corpus: Although ICE Nigeria’s descriptive mean was higher for each model, the overall corpus effect in the fitted model did not reach statistical significance. The averages should not be recast as proof that Whisper is inherently less accurate for Nigerian English in general.
- Recording quality: Good-quality audio was associated with a 25% decrease in WER in the fitted model.
- Number of speakers: Poly-speaker recordings had roughly 28% higher WER than monologues in the fitted model.
- Speech category and individual files: Category mattered, and individual results varied widely; some files had WER above 1.0.
The 25% and roughly 28% figures are model associations reported for this sample, not universal adjustments that can be applied to any recording. The observed file-level spread is why a mean score cannot tell you whether a particular transcript is reliable.
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How to use the findings when checking a transcript
For a research-grade transcript, a quotation, or any work where wording and speaker attribution matter, compare the output with the audio rather than accepting fluent text as evidence of fidelity. A practical review should prioritize the parts of the recording most likely to diverge from a verbatim reference:
- Listen for omitted hesitations, repetitions and false starts if the transcript must preserve them.
- Check repeated phrases against the sound, especially around silence, interruptions and overlapping speech.
- Review switches into Nigerian Pidgin rather than assuming that all speech in the Nigerian sample is Nigerian English.
- Check speaker labels and timestamps separately if your transcription workflow adds them.
The needed level of review depends on the task: a readable summary and a sociolinguistic verbatim transcript have different standards for what should be preserved.
What the benchmark does—and does not—say about current Whisper
This is a benchmark of six model variants available for Weilinghoff’s 2025 study, using a particular sample, reference convention and plain-text output. It does not establish that a newer model, a hosted API or a different transcription setup would reproduce these scores.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsOpenAI’s current documentation is a separate, changing guide to model choice. The Whisper repository lists model sizes, approximate VRAM requirements and relative speeds measured on an A100, while cautioning that real-world speed depends on language, speaking speed and hardware. Current API documentation recommends gpt-transcribe for general recorded speech and notes Whisper’s relevance for use cases such as word- or segment-level timestamps. Those recommendations are not a rerun or direct comparison with the ICE benchmark.
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