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To evaluate word error rate (WER) in a brain-to-text system, calculate the edits needed to turn the reference transcript into the system’s output, then divide by the number of words in the reference. A WER is meaningful only when reported with the task, participants, test split, vocabulary, decoder and language-model pipeline, aggregation method, and uncertainty. Scores from different conditions are not automatically comparable.
How do you calculate word error rate?
Align the system’s hypothesis (its output) with the reference transcript. Count the word-level substitutions, deletions, and insertions needed to transform the reference into the hypothesis:
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WER = (S + D + I) / N
- S: words replaced by a different word.
- D: reference words missing from the hypothesis.
- I: words present in the hypothesis but absent from the reference.
- N: number of words in the reference.
Multiply by 100 to express WER as a percentage. Because insertions add errors without increasing the reference-word denominator, WER can exceed 100%. It is therefore an edit-rate measure, not the percentage of words understood correctly. The foundational Brain-To-Text paper defines the metric in these terms.
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WER depends on how the reference and output are prepared and tokenized. Report whether punctuation, capitalization, disfluencies, and partial or unfinished utterances are normalized or counted, and how reference words are tokenized. There is no single convention established across all brain-to-text studies; use the protocol for the specific study or benchmark rather than assuming one.
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How should trials be combined?
State whether the reported score is corpus-level WER or an average of sentence-level WERs. For corpus-level WER, sum substitutions, deletions, and insertions across the test set, then divide by the total number of reference words. This weights trials according to their reference length. Averaging each sentence’s WER instead gives each sentence equal weight, even if sentence lengths differ, and can yield another result.
Report the participant count, number of test trials, total reference-word count, point estimate, and uncertainty interval. Describe how uncertainty was estimated. For example, a 2026 bioRxiv preprint pools errors across trials and estimates confidence intervals with 10,000 bootstrap resamples of individual trials; that is the method in that study, not a universal requirement. See the preprint.
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What must be reported to make a WER interpretable?
A useful result describes the conditions that produced the score, not just the percentage. Include the following where available:
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- Speech task: attempted, overt, or imagined speech; prompted or conversational speech; and whether communication was open-loop or closed-loop.
- Vocabulary and language context: vocabulary size, prompt construction, language-model constraints, and whether test text appeared during training.
- Test split and time horizon: held-out trials, sentences, sessions, days, or participants; also state what calibration data were available for each test condition.
- Recording and decoder: neural recording setup and each decoding stage, including intermediate phoneme or character representations, vocabulary constraints, beam search, language-model rescoring, and final text generation.
- Scoring and aggregation: normalization, tokenization, exclusions, pooled or sentence-averaged calculation, reference-word count, and confidence interval method.
- Practical use: communication rate, latency, correction burden, and error types when measured.
The output pipeline matters because final text can reflect both neural decoding and language-model or post-processing choices. Without those details, a difference in WER cannot be attributed to the neural decoder alone.
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Can you compare WER across different brain-to-text studies?
Only compare scores as direct head-to-head results when the relevant evaluation conditions are aligned. In practice, many published results differ on several axes at once:
- Vocabulary: a small constrained vocabulary is a different task from large-vocabulary or open decoding.
- Participants and speech task: one participant attempting speech cannot be treated as equivalent to a cohort performing imagined speech.
- Generalization: held-out trials from a familiar session do not test the same thing as held-out days or participants.
- Language-model support: different constraints, training exposure, and rescoring can change final WER.
- Scoring protocol: tokenization, normalization, exclusions, and aggregation can change the result.
Published figures illustrate why labels must stay attached to scores:
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| Reported result | What it describes | How to interpret it |
|---|---|---|
| 9.1% WER for a 50-word vocabulary; 23.8% for a 125,000-word vocabulary | One participant in a 2023 Nature neuroprosthesis study | Keep each vocabulary with its score. These results illustrate performance in distinct settings, not a controlled experiment isolating vocabulary size. Nature paper |
| 0.44% WER over 50 evaluation sentences | An initial closed-loop session in a 2023 medRxiv report, following 213 training sentences, with a 50-word vocabulary | A result for that session and protocol; it does not establish broad-vocabulary or cross-participant performance. medRxiv report |
| 5.77% WER versus 8.93% | A 2024 Brain-to-Text benchmark comparison described in a 2025 PubMed-indexed article; the lower figure used a fine-tuned language model and the other was the leading method in that paper | A paper-specific comparison that also reflects language-model design, not a general leaderboard ranking. PubMed record |
| 24.69% to 10.22% end-to-end WER | The 2026 ICLR BIT paper’s comparison with a prior end-to-end method under its evaluation | Keep the figures tied to that paper’s benchmark and conditions; do not treat them as a field-wide score. ICLR paper |
A particularly low score on a tightly constrained task does not, by itself, show that a system will perform similarly with a larger vocabulary, in a new session, or for another participant. Likewise, a lower score in a paper’s benchmark comparison does not establish that the method leads under other datasets or protocols.
What should accompany WER?
WER treats every word edit equally. It does not reveal whether an error changes meaning, whether performance is worse on infrequent words, or how quickly a person can communicate.
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- Phoneme error rate (PER) and character error rate (CER) show errors at smaller units than whole words. They complement WER rather than replace it.
- Words per minute measures throughput. A system’s speed should be reported separately from its accuracy.
- Error analysis can identify which words are mistaken, omitted, or inserted and whether errors have unequal effects on meaning.
A 2025 Interspeech study introduces refined word-level alignment and four additional metrics for exact correctness and semantic distance. It reports frequency-related performance differences and greater semantic cost for errors on infrequent words. When usable communication is the goal, a word-frequency breakdown or task-relevant error analysis can therefore add information that one WER value cannot. Interspeech study
How to read a reported WER
- Check the task and vocabulary. Identify the speech mode, prompt setting, vocabulary size, and language-model constraints.
- Check what was held out. Determine whether the test used unseen trials, sentences, sessions, days, or participants, and what calibration data were available.
- Check the pipeline. Find out whether the score is for neural decoding alone or final text after intermediate decoding, constraints, and language-model rescoring.
- Check the calculation. Confirm tokenization and normalization, pooled versus sentence-averaged aggregation, reference-word count, and uncertainty method.
- Check practical outcomes. Read WER alongside rate, latency, corrections, and error analysis where reported.
Benchmark claims also need an edition and held-out protocol. The figures above describe particular papers and comparisons; they do not establish the current official challenge leader or a universal benchmark scoring rule. For a current leaderboard claim, consult the challenge organizers’ documentation for the relevant edition.
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