Repetition, Not Length: Isolating the Counting Failure in Neural Text-to-Speech
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 predicte...
| Authors | Kirill Borodin, Vasilii Kudryavtsev, Maxim Maslov et al. |
|---|---|
| Primary category | cs.CL |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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