Text-to-speech models loop, truncate and lose count on text that repeats a phrase many times.
We show that repetition itself is what breaks them, not the length that comes with it.
Every repeated sentence in our test set is paired with a control of matched sentence and word count in which no word ever repeats back-to-back.
Six models from three architectures render the controls almost perfectly and fail the repeated twins: 94.3% against 18.2% exactly right at k >= 6.
The gap survives greedy decoding, repetition-penalty sweeps, four independent speech recognisers and 420 analysis specifications without once reversing sign;
a held-out fourth architecture lands within a point of its predicted gap, and one of two non-autoregressive baselines shows the same failure.
Varying the period of the text shows the failure grows smoothly with periodicity, half of it surviving when no word is adjacent to itself.