# Method

## The test set

1,548 clips, 4.72 hours, drawn from the test split of an open Amharic speech
corpus of read prompts.

**The labels are certain.** A contributor was shown a sentence and read it
aloud, so the reference text existed before the audio did. This is unusual and
it matters: most speech benchmarks score against transcripts a person typed
while listening, which carries its own error rate that nobody measures. Here
there is no transcriber to be wrong.

**The splits are speaker-disjoint.** No speaker appears in more than one split,
so a model cannot score well by having memorised a voice.

## No contamination

Every model evaluated was published **before** the test set existed. Checked
against creation timestamps, not asserted:

| asset | published |
|---|---|
| **the test set** | **2026-08-25** |
| `badrex/Ethio-ASR-*` (all variants) | 2026-03-24 |
| `b1n1yam/shook-medium-amharic-2k` | 2025-12-08 |
| `b1n1yam/shook-tiny-*` | 2025-11-21 |
| `openai/whisper-large-v3` | 2023-11 |

This is the property that makes the numbers worth quoting. A benchmark whose
test data may sit in the training set of the models it ranks is measuring
memory, not recognition.

## Scoring

**Character error rate is the headline metric.** Amharic is agglutinative:
prefixes, suffixes and clitics attach to a stem, so a single wrong affix on a
long word costs an entire word under WER and discards most of the signal. Word
error rate is reported alongside it because it is what everyone else quotes and
comparability matters more than being right about which metric is better.

Before scoring, both reference and hypothesis pass through the corpus's own
published evaluation script, which:

- folds Ge'ez homophone families that carry no phonemic distinction in modern
  usage (ሀ/ሐ/ኀ, ሰ/ሠ, አ/ዐ, ጸ/ፀ)
- strips Ethiopic punctuation, which lives in the same Unicode block as letters
  and so is not caught by generic punctuation classes
- normalises to NFC

One model performs joint recognition and language identification and prefixes
its output with a tag such as `[AMH]`. That tag is stripped before scoring.
Leaving it in charges the model for doing the second job it was designed to do,
and in an earlier experiment it silently affected 290 of 500 outputs.

Normalisation is applied **for scoring only**. Folding ሐ into ሀ is correct when
comparing two strings and wrong as a training target: a model trained on folded
text learns to misspell.

## Decoding

Greedy, no beam search, for every model. This is a deliberate limitation rather
than a claim of optimality, and it is stated because it is not neutral: CTC and
sequence-to-sequence models are not equally penalised by it. Sequence models are
normally run with beam search and were not here. **Treat every number as a
conservative lower bound**, and treat the CTC-versus-Whisper comparison with
more caution than the comparisons within each family.

Sequence models are generated with a 440-token limit. An earlier pass used 200
and truncated them; see `FAILURES.md`.

## Statistics

Point estimates are not enough to say one model beats another.

- **95% confidence intervals** come from a bootstrap over 2,000 resamples of
  **clips**, not characters, so the unit of resampling matches the unit of
  independence.
- **Comparisons use a paired bootstrap.** Every model is scored on the same
  clips, so the difference between two models is computed within each resample
  rather than by comparing marginal intervals.

This distinction is not academic here. Our top two models have intervals of
[0.0896, 0.0999] and [0.0939, 0.1042], which overlap, and the paired test finds
the difference real at p = 0.001. Reading the marginal intervals alone would
have produced the wrong conclusion.

## Reproducing

`hypotheses/` contains every model's raw output for all 1,548 clips. You do not
have to trust these numbers: rerun `scripts/bootstrap_ci.py` against them, or
score them with your own normalisation and see what changes.