# Limitations Read this before quoting anything here. ## 1. One domain, and it is the easy one Every number is measured on **people reading prompts aloud into a phone**. One speaker, close microphone, scripted grammatical sentence, no overlap, no music. That is not how anyone talks. We measured the gap on spontaneous podcast audio using teacher agreement as a proxy: the same models disagree with each other **roughly three times more** on spontaneous speech than on read prompts (mean pairwise distance 0.32 against 0.10). We could not put an absolute error rate on it because we had no ground truth for spontaneous speech. **Do not quote these figures as general Amharic ASR performance.** They describe one domain, and it is the domain most favourable to every model in the table. ## 2. Greedy decoding only No beam search, no language model in the base numbers, no hyperparameter search per model. Every figure is a lower bound on what the model can do. This limitation is **not evenly distributed**. Sequence-to-sequence models are usually deployed with beam search and gain more from it than CTC models typically do. The within-family comparisons are sound; the cross-family comparison should be treated as provisional. ## 3. Normalisation is a choice, and it moves the numbers Folding Ge'ez homophone families is defensible — those characters are not distinguished in modern pronunciation — but it is a choice, and a stricter scorer would produce higher error rates for everyone. The script is published so the choice is inspectable rather than implicit. Numbers from a different normaliser are not comparable to these. ## 4. Small by benchmark standards 1,548 clips and 4.72 hours. Enough to separate models that differ by more than about half a point of CER, as the confidence intervals show, and **not** enough to rank models that are genuinely close. Where the paired test says a difference is not distinguishable, treat it as a tie however different the point estimates look. ## 5. Single run, no seed variance Each model was evaluated once. Greedy decoding is deterministic, so there is no sampling variance to average over, but nor is there any check against a transient environment problem. The full inputs and outputs are published so that an independent rerun is cheap. ## 6. What is not measured at all - Real-time factor, latency, memory. A model that is slightly better and ten times slower is not better for most deployments. - Robustness to noise, telephone bandwidth, or code-switching into English, which is extremely common in Ethiopian speech. - Speaker demographics beyond what the corpus records. If a model is worse for women, or for particular regional accents, nothing here would reveal it. That last one is a real gap and we intend to close it, since the corpus carries gender, age band and region for every clip.