Repetition, Not Length: Isolating the Counting Failure in Neural Text-to-Speech
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Computer Science > Computation and Language
arXiv:2609.36974 (cs)
[Submitted on 29 Sep 2026]
Title:Repetition, Not Length: Isolating the Counting Failure in Neural Text-to-Speech
View a PDF of the paper titled Repetition, Not Length: Isolating the Counting Failure in Neural Text-to-Speech, by Kirill Borodin and 3 other authors
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Abstract: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.
| Comments: | Submitted to IEEE ICASSP 2027. Code and data: this https URL |
| Subjects: | Computation and Language (cs.CL); Sound (cs.SD) |
| Cite as: | arXiv:2609.36974 [cs.CL] |
| (or arXiv:2609.36974v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.36974
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Repetition, Not Length: Isolating the Counting Failure in Neural Text-to-Speech, by Kirill Borodin and 3 other authors
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