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@@ -62,3 +62,30 @@ an independent rerun is cheap.
 
 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.
+
+## 7. The language model damages code-switched speech
+
+Found by talking to the demo rather than by any measurement here.
+
+Recording a sentence that mixed English into Amharic, the way people actually
+speak in Addis, the speech model heard "my name is" and transliterated it
+phonetically into Ge'ez, which was correct. The language model then changed it
+into something else.
+
+It was confidently correcting a word that was already right. This is not a bug,
+it is the language model being what it is: trained on 12.8 million lines of
+**Amharic** text, in which transliterated English never appears. Seeing one, it
+concludes the speech model must have erred and pulls toward something more
+Amharic-shaped.
+
+**The more English a clip contains, the worse that trade becomes.** Since
+English code-switching is extremely common in Ethiopian speech, this is a real
+limitation and not an edge case.
+
+Nothing in this benchmark could have caught it. The test sentences come from
+news text and contain almost no English. It took one person speaking naturally
+for thirty seconds.
+
+The fix is to train the language model on Amharic as it is actually spoken,
+English included, which requires transcripts of spontaneous speech. That is the
+same missing ingredient as limitation 1.