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.
Figures & tables
Scorer
NLL/token
ctl. less probable
rep.
ctl.
(% of pairs)
phi-2
3.69
4.89
87
GPT-2-large
3.36
5.34
100
Table 1: Likelihood of the paired texts under two independent scorers: mean NLL per token (lower = more probable) and the share of 54 pairs whose control is less probable. A panel-internal scorer agrees (supplementary material).
Quantity
est.
95% interval
Exact-rate gap, per checkpoint ( n=6 )
76.7
[68.4,84.4]
Exact-rate gap, per family ( n=3 )
75.4
[71.1,78.9]
Exact-rate gap, mixed effects (widest of six)
76.4
[63.2,89.2]
Count-error gap, per checkpoint
10.4
[5.6,13.9]
Exact-rate gap, whole ladder ( n=1559 )
46.8
—
Predicted gap, unseen architecture
65.3
[5.9,93.3]
Table 2: Headline estimates with 95% intervals, in exactly-right points (the count-error row: points of median relative error). The last two rows are the pre-registered out-of-panel prediction and its test.
Model
size
count err.
exact (%)
rep.
ctl.
rep.
ctl.
Llasa-1B [ 20 ]
1B
−12.5
0.0
13.3
91.5
Llasa-3B [ 20 ]
3B
−8.3
0.0
16.9
94.1
Llasa-8B [ 20 ]
8B
−16.7
0.0
11.8
93.2
XTTS-v2 [ 2 ]
0.4B
−12.5
0.0
16.7
87.8
Qwen3-TTS-0.6B [ 8 ]
0.6B
−12.5
0.0
7.9
100.0
Table 3: Every system tested, k≥6 , CTC judge: median (c^−k)/k and exactly-right rate, repeated (rep.) against control (ctl.). Systems in the lower block are outside the panel; their count errors are in the supplementary material.
Requested count
repeated
control
k=48
25.0
46.0
k=64
25.0
59.5
k=96
22.0
64.0
k=128
17.0
66.5
Table 4: The k≤128 extension, 4 checkpoints: median rendered count against the requested count. Repeated renderings regress while controls advance.
p=1
p=2
p=3
p=4
p=6
p=8
Ladder scan ( n=1539 )
10.4
47.9
—
73.6
—
93.1
At k=24 ( n=1202 )
8.5
51.2
62.3
75.2
90.0
92.9
Table 5: The period ladder: exactly-right rate (%) against the period p of the text at fixed carrier, word count and requested count, pooled over the four checkpoints measured. The second row re-measures at k=24 with the odd and non-power steps added.
gap (pts)
Panel, k≥6
76.1
Exclusions off ( n=1620 )
61.7
No penalty (Llasa × 3)
78.9
Greedy (Qwen3-TTS-0.6B)
72.2
Sampled (Qwen3-TTS-0.6B)
75.0
RAS on (Qwen3-TTS-0.6B)
94.3
Table 6: The gap under decoding and analysis variations. RAS is repetition-aware sampling [ 3 ] .
free
oracle duration
Below k=12 ( n=30 )
43.3
76.7
At or above k=12 ( n=60 )
16.7
16.7
Table 7: F5-TTS handed the oracle total duration: exactly-right rate (%).
Model
capacity gain
ceiling
rep.
ctl.
rep.
ctl.
Llasa-1B [ 20 ]
18.2 [9.8,25.7]
56.7 [50.9,63.5]
181
247
Llasa-3B [ 20 ]
12.4 [−1.8,23.3]
58.9 [52.8,68.0]
178
249
Llasa-8B [ 20 ]
15.0 [11.0,20.3]
45.5 [37.2,53.1]
188
228
XTTS-v2 [ 2 ]
24.0 [16.9,31.1]
34.8 [29.1,40.7]
81
105
Qwen3-TTS-0.6B [ 8 ]
20.9 [16.3,25.9]
34.9 [30.6,39.6]
81
115
Table 8: State capacity per checkpoint: growth of distinguishable states dNeff/dlogk with 95% intervals, and the saturating-fit ceiling of Neff , repeated against control.
Checkpoint
repeated
control
Llasa-1B [ 20 ]
1.01
0.86
Llasa-3B [ 20 ]
0.99
0.83
Llasa-8B [ 20 ]
0.86
0.59
Qwen3-TTS-0.6B [ 8 ]
0.74
0.95
Qwen3-TTS-1.7B [ 8 ]
0.90
0.89
Table 9: Probe MAE past the ladder ( k=48 – 128 ) as a fraction of a constant predictor’s; below 1 the count is still decodable. The control column runs the same probe on control items.
Appendix figures & tables35 assets
Supplementary material from the paper’s appendix.
Appendix
Family
Count
What it tests
word repetition
60
a single word repeated k times in a fixed carrier
matched control
54
the same carrier, k distinct filler words (no repetition)
sentence repetition
32
a whole short sentence repeated k times
tongue twisters
24
a twister sentence (with internal repetition) repeated k times
numbers
10
English number phrases with intrinsic digit-word repetition
Table S3: Number-phrase items, with the target digit-word and its true count.
ID
Sentence
Target
Per-copy
s1
The bell rang twice.
bell
—
s2
He counted the stones.
stones
—
s3
Rain fell on the roof.
rain
—
s4
The door stayed open.
door
—
w1
She sells sea shells by the sea shore.
sea
2
w2
Peter Piper picked a peck of pickled peppers.
peck
1
Appendix
Table S4: Sentence-repetition items ( k∈{1,2,3,4,6,8,12,16} , expected count k ) and tongue-twister items ( k∈{1,2,4} , expected count per-copy occurrences of the target ×k ).
Recogniser
Material
k≥4 ratio
Notes
CTC
periodic (repeated word)
1.00
22/24 exact; 2 off by 1 ( k=16,32 )
CTC
repeated sentence (control)
1.00
23/24 exact; 1 off by 1 ( k=32 )
Whisper large-v3
periodic (repeated word)
0.19
no transcript at k=16,32
Whisper large-v3
repeated sentence (control)
0.25
no transcript at k=16,32
Appendix
Table S5: Head-to-head validation on identical synthetic ground truth. Median counted/true ratio, k≥4 pooled. The repeated-sentence arm is periodic at the sentence grain, not an aperiodic control.
primary
HuBERT
robust-ft
Whisper
control exact (%)
94.3
89.5
82.1
92.9
repeated exact (%)
18.2
17.9
17.5
17.1
gap (points)
+76.7
+72.3
+65.1
+75.5
Llasa-1B
78.1
78.6
65.2
69.9
Llasa-3B
77.2
74.8
65.9
76.0
Llasa-8B
81.4
80.3
69.7
71.7
Appendix
Table S6: Four judges on the full panel population: exact rates and repeated-versus-control gap in points ( k≥6 , paired panel), then the gap by checkpoint and by family.
checkpoint
exact-rate (pts)
median-error (pts)
capacity
Llasa-1B [ 20 ]
+78.1
+12.5
+38.5
Llasa-3B
+77.2
+8.3
+46.5
Llasa-8B
+81.4
+16.7
+30.5
Qwen3-TTS-0.6B [ 8 ]
+92.2
+12.5
+14.0
Qwen3-TTS-1.7B
+60.0
+0.0
+8.1
XTTS-v2 [ 2 ]
+71.1
+12.5
+10.7
Appendix
Table S7: The three per-checkpoint contrasts, signed so positive means the deficit is present: exact-rate and median-error gaps in percentage points, the capacity gap in distinguishable states per unit logk .
contrast
mean
95% CI
p
positive
exact-rate gap (pts)
76.7
[68.4, 84.4]
0.031
6/6
median-error gap (pts)
10.4
[5.6, 13.9]
0.062
5/6
capacity gap
24.7
[13.7, 36.5]
0.031
6/6
Appendix
Table S8: Panel-level summary of the three contrasts: mean, bootstrap 95% CI, two-sided exact sign-test p , and the count of positive checkpoints.
Construction
n clusters
Estimate
95% interval
Criterion
Mixed model, crossed RE, Wald (reference only)
3 fam.
76.4
[71.9, 80.9]
z
the six constructions the main text’s “all six” counts:
Mixed model, crossed RE, t(2)
3 fam.
76.4
[66.6, 86.3]
t(2)
OLS, clustered by family
3
76.2
[68.7, 83.7]
t(2)
OLS, clustered by checkpoint
6
76.2
[63.2, 89.2]
t(5)
Family-level means, t -interval
3
75.4
[65.6, 85.2]
t(2)
Appendix
Table S9: Interval constructions for the arm effect (control − repeated, percentage points). The Wald row is reported for reference and is not one of the six intervals the main text’s “all six” refers to.
Level
Unit
Mean
95% credible interval
Family
Llasa
79.3
[73.4, 84.4]
Family
Qwen3-TTS
74.1
[67.7, 79.9]
Family
XTTS
73.7
[63.5, 82.2]
Checkpoint
Llasa-1B
79.5
[70.5, 86.9]
Checkpoint
Llasa-3B
77.2
[67.0, 85.5]
Checkpoint
Llasa-8B
81.0
[72.7, 88.0]
Appendix
Table S10: Posterior arm-effect gap (percentage points, control − repeated), partially pooled; all P(gap>0)=1.000 to the precision shown. Llasa [ 20 ] , Qwen3-TTS [ 8 ] , XTTS-v2 [ 2 ] .
Prior on σ
Mean
95% CrI
P(gap>0)
HalfNormal(0.5), tighter, favourable
71.7
[26.6, 90.9]
1.000
HalfNormal(1.0), reported
65.3
[5.9, 93.3]
0.996
HalfNormal(3.0), much wider, unfavourable
52.3
[ − 1.6, 96.1]
0.956
Appendix
Table S11: Prior sensitivity on the four group-level scales σ : new-family predictive gap (points).
Outcome
n
Range (pts)
Median
Positive
Zero / negative
Exact-rate gap
210
[34.4, 86.4]
70.5
210/210
0 / 0
Median-error gap
210
[0.0, 19.5]
8.3
197/210
13 / 0
Both outcomes combined
420
—
—
407/420
13 / 0
Appendix
Table S12: Specification curve; n is the number of specifications under that outcome definition.
Table S13: Which forking choice moves the exact-rate gap (median gap at each level, percentage points), sorted by spread, descending.
model
sat. SSE
prop. SSE
b
soft N∗
best
Llasa-1B [ 20 ]
477.5
35.0
0.855
85
proportional
Llasa-3B
464.2
79.3
0.913
119
soft horizon
Llasa-8B
353.5
2.0
0.830
71
proportional
Qwen3-TTS-0.6B [ 8 ]
499.3
1.4
0.953
338
proportional
Qwen3-TTS-1.7B
668.8
5.1
1.107
640
proportional
XTTS-v2 [ 2 ]
378.0
0.8
0.860
90
proportional
Appendix
Table S14: Constant, proportional and soft-horizon fits to the median rendered count above k=6 : residual sums of squares, fitted slope b , fitted horizon N∗ , and the winning form.
arm
template rule
token budget
degenerate
repeated ( n=1080 )
16.7%
10.5%
4.4%
control ( n=972 )
16.7%
5.6%
3.7%
Appendix
Table S15: Exclusion rates by arm, before any other filter.
population
n
exact rep
exact ctl
gap
panel (as reported)
1234
18.2%
94.3%
76.1
+ judge-unmeasurable template
1424
17.2%
79.8%
62.6
+ budget-truncated items
1362
16.3%
93.1%
76.7
+ degenerate audio (scored 0)
1242
17.9%
93.9%
76.0
nothing excluded at all
1620
14.8%
76.5%
61.7
Appendix
\fnum@table : The gap with each exclusion rule lifted in turn, and with nothing excluded at all.
Scorer
Role
Rep. mean
Ctl. mean
Gap
Ctl. higher
Llasa-1B
panel backbone
14.85
16.05
1.20
89%
phi-2
independent, primary
3.69
4.89
1.20
87%
gpt2-large
secondary cross-check
3.36
5.34
1.98
100%
Appendix
\fnum@table : Naturalness check, three scorers, 54 matched pairs. “Ctl. higher”: fraction of pairs in which the control has the higher (less probable) per-token cross-entropy.
Model
kword∗
tok. (word)
ksent∗
tok. (sent)
Llasa-1B
0
n/a
3
351
Llasa-3B
1
209
3
457
Qwen3-TTS-0.6B
4
53
4
80
Qwen3-TTS-1.7B
8
80
4
86
XTTS-v2
4
89
4
128
XTTS-v2 (no penalty, ablation)
2
63
1
29
Appendix
\fnum@table : Unit invariance, as committed. Token counts are medians at the reported k∗ ; n/a where no k cleared the 0.5 threshold.
Model
Raw
+diversity
correct-only
Llasa-1B
0.42
0.50
0.01 ( n=14 /32)
Llasa-3B
0.28
0.34
0.80 ( n=24 /22)
Llasa-8B
0.43
0.75
— ( n=0 /0)
Qwen3-TTS-0.6B
0.57
—
0.31 ( n=42 /84)
Qwen3-TTS-1.7B
0.78
—
0.47 ( n=64 /82)
XTTS-v2
0.60
—
0.56 ( n=22 /41)
Appendix
\fnum@table : Capacity-confound ratios (repeated/control slope of Neff against logk ). n pairs are repeated-side/control-side rows entering each regression; — marks an inestimable cell (fewer than 12 usable rows, or fewer than 4 distinct k values).
\fnum@table : The repetition-penalty sweep on the two architectures that ship one; a collapsed control arm says nothing about repetition.
checkpoint
p=1
p=2
p=4
p=8
p=k
n ( 1,2,4,8,k )
Llasa-1B
3.6
54.6
83.3
92.5
75.0
28, 130, 84, 40, 44
Qwen3-TTS-0.6B
6.8
41.3
73.3
100.0
86.7
44, 179, 90, 45, 45
XTTS-v2
11.1
51.7
75.6
82.2
57.8
45, 180, 90, 45, 45
MEAN
7.2
49.2
77.4
91.6
73.2
Appendix
\fnum@table : The full period ladder: exactly-right rate (%) by period at k∈{16,24,32} under the ordered-scan rule, with cell sizes.
rule
D(1)
D(2)
D(4)
D(2)/D(1)
verdict
ordered scan (headline)
84.4
42.4
14.2
0.502
PERIODICITY
unbounded recount
82.9
60.9
20.1
0.734
PERIODICITY
strict conjunction
82.9
61.2
21.2
0.738
PERIODICITY
cap hits kept (censored bound)
81.9
43.5
13.5
0.531
PERIODICITY
by family
84.4
42.4
14.2
0.502
PERIODICITY
Appendix
\fnum@table : The period-2 recovery under five scoring rules: deficits D(p)=E(8)−E(p) in points, the decisive ratio D(2)/D(1) , and the pre-committed verdict.
exact rate
recovered R
confound means
variant
Qwen3
XTTS
Qwen3
XTTS
words
chars
dur. (s)
bare
6.8
11.1
—
—
31.8
170.6
11.65
comma
6.8
8.9
+0.000
−0.031
31.8
194.3
13.11
stop
9.8
13.6
+0.031
+0.036
31.6
191.6
13.42
and
15.6
11.1
+0.094
+0.000
54.8
263.0
16.23
Appendix
\fnum@table : The disambiguation arm at period 1: exactly-right rate and recovered fraction R by boundary variant, with per-variant confound means.
adjacent steps
gap (pts)
95% interval
E(3)−E(2)
+10.9
[+2.1,+16.8]
E(4)−E(3)
+12.9
[+3.6,+21.2]
E(6)−E(4)
+14.7
[+2.2,+28.3]
E(8)−E(6)
+2.7
[−2.4,+7.7]
Appendix
\fnum@table : Adjacent-step gaps in exactly-right points on the k=24 ladder, carrier-cluster bootstrap 95% intervals.
checkpoint
E periodic
E shuffled
Δ
95% CI
Qwen3-TTS-1.7B
80.0
97.8
+17.8
[+5.6,+34.4]
Qwen3-TTS-0.6B
73.3
91.1
+17.8
[+6.7,+32.2]
Llasa-1B
74.1
68.9
−5.2
[−22.0,+11.3]
XTTS-v2
73.3
43.3
−30.0
[−53.3,−6.7]
Appendix
\fnum@table : Shuffled order at fixed composition: exactly-right rate on periodic and shuffled p=4 items per checkpoint, with the bootstrap 95% interval on the difference.
model
repeated exact (%)
control exact (%)
gap (pts)
Llasa-1B
13.3
91.5
+78.1
Llasa-3B
16.9
94.1
+77.2
Llasa-8B
11.8
93.2
+81.4
Qwen3-TTS-0.6B
7.8
100.0
+92.2
Qwen3-TTS-1.7B
38.9
98.9
+60.0
XTTS-v2
16.7
87.8
+71.1
Appendix
\fnum@table : The published arms on the two non-autoregressive baselines, beside the panel: exactly-right rates and the gap in points.
arm
setting
exact rep
exact ctl
gap
stock
—
67.6%
49.1%
−18.5
duration
duration-noise scale 1.6
53.7%
20.4%
−33.3
rate
speaking rate 1.35
61.1%
31.5%
−29.6
Appendix
\fnum@table : VITS under perturbed duration settings; the gap is control minus repeated, negative in every arm.
k
free exact (%)
given exact (%)
free median
given median
6
46.7
80.0
-0.167
+0.000
8
40.0
73.3
-0.125
+0.000
12
33.3
40.0
-0.083
-0.083
16
20.0
26.7
-0.062
-0.062
24
6.7
0.0
-0.167
-0.125
32
6.7
0.0
-0.094
-0.125
Appendix
\fnum@table : F5-TTS handed the oracle total duration: exactly-right rate and median relative error, free against given, by k .
checkpoint
layers
n pairs
q repeated [95% CI]
q control [95% CI]
paired p
Llasa-1B
16
29
38.1 [32.9, 51.4]
31.8 [27.3, 39.8]
0.017
Llasa-3B
28
26
118.8 [101.9, 156.5]
86.4 [77.2, 137.3]
0.269
Llasa-8B
32
29
347.7 [258.1, 393.5]
185.4 [142.1, 236.5]
0.256
Qwen3-TTS-0.6B
28
44
21.0 [19.7, 24.2]
21.9 [20.6, 23.2]
0.931
Qwen3-TTS-1.7B
28
45
24.7 [23.1, 25.7]
23.6 [21.4, 25.1]
0.177
Appendix
\fnum@table : Largest singular value q of the per-repetition map at the headline setting (lag τ , whole stack), per arm, with the paired repeated-versus-control test. Checkpoints as in the main text’s panel table.
checkpoint
repeated: range over 16 cells
control: range over 16 cells
Llasa-1B
[3.90, 78.10]
[3.50, 46.61]
Llasa-3B
[9.33, 182.80]
[7.60, 148.28]
Llasa-8B
[11.43, 499.13]
[12.28, 329.33]
Qwen3-TTS-0.6B
[3.35, 29.47]
[3.41, 28.23]
Qwen3-TTS-1.7B
[3.45, 37.83]
[3.35, 31.18]
Appendix
\fnum@table : Range of q over all sixteen (lag × sub-stack) cells, per arm. Checkpoints as in the main text’s panel table.
flatness measure
repeated
control
largest single share
+0.59 [+0.35, +0.75]
+0.01 [-0.34, +0.37]
block entropy
+0.43 [+0.16, +0.65]
+0.17 [-0.20, +0.49]
deviation from uniform
+0.55 [+0.30, +0.72]
+0.05 [-0.31, +0.39]
Appendix
\fnum@table : Within-cell correlation between attention flatness and count error, pooled by Fisher z ; positive means flatter items are counted better.
checkpoint
arm
probe MAE
constant MAE
R2
Llasa-1B
repeated k=2…32
0.63
1.12
+0.62
Llasa-3B
repeated k=2…32
0.86
1.12
+0.41
Llasa-8B
repeated k=2…32
0.75
1.12
+0.52
Qwen3-TTS-0.6B
repeated k=2…32
0.79
1.12
+0.47
Qwen3-TTS-1.7B
repeated k=2…32
0.79
1.12
+0.47
Llasa-1B
repeated k=48…128
0.50
0.50
−0.02
Appendix
\fnum@table : Ridge-probe decoding of the requested count against the constant predictor, below the horizon ( k≤32 ) and past it ( k=48…128 ).
MAE ratio vs. constant
MAE, repeated arm
checkpoint
rep.
reading
ctl.
length-only
state probe
Llasa-1B
1.01
indistinguishable
0.86
0.61
0.51
Llasa-3B
0.99
indistinguishable
0.83
0.56
0.49
Llasa-8B
0.86
recovers the count
0.59
0.45
0.43
Qwen3-TTS-0.6B
0.74
recovers the count
0.95
0.30
0.37
Qwen3-TTS-1.7B
0.90
recovers the count
0.89
0.36
0.45
Appendix
\fnum@table : Past-horizon verdicts: probe-to-constant MAE ratios with the ±0.02 tie band, and the length-only control on the repeated arm.
checkpoint
P1 ratio
worst cell
bound
verdict
Qwen3-TTS-0.6B
1.38
14%
9%
readable null
Qwen3-TTS-1.7B
1.12
9%
6%
readable null
Llasa-1B
0.57
25%
20%
readable null
Llasa-8B
0.90
42%
—
cannot tell
Appendix
\fnum@table : Rank-1 count-coordinate patching on four checkpoints in two families [ 20 , 8 ] , 2,178 scored runs, thresholds pre-committed before any audio. P1 ratio: unrelated-to-informative disruption. Worst cell: largest median shift across donor kinds as a fraction of a full donor-to-receiver transfer (noise-floor bar: below 25% ). Bound: 90% TOST equivalence bound on the informative cross- k cell in the same units, the smallest excludable effect.
Large language models fail at counting repeated tokens despite strong performance on broader reasoning benchmarks. These failures are commonly attributed to limitations in internal count tracking. We show this attribution is wrong. Linear probes on the residual stream decode the correct count with near-perfect accuracy at every post-embedding layer, across all model depths. This holds even at the exact layers where the wrong answer crystallizes while the model simultaneously outputs an incorrect count. Attention patterns show no evidence of collapse over repeated tokens and tokenization artifacts account for none of the failure. Instead, a format-triggered multi-layer perceptron (MLP) block overwrites the correctly-encoded count with a fixed wrong answer at roughly 88--93,% network depth. This prior fires for repeated word-tokens in space-separated list format and is absent for repeated digit-tokens. It is suppressed by comma-separated delimiters in larger models but persists in smaller ones. The finding holds across Llama-3.2 (1B and 3B) and Qwen2.5 (1.5B, 3B and 7B) at consistent relative depth. Counting failure is a failure of routing not of representation and the two require different interventions.
Long-form text-to-speech (TTS) enables multi-turn conversations with consistent prosody and higher quality voice cloning from longer reference audio. Recent open-weights autoregressive TTS models such as Qwen3-TTS and VoxCPM2 attain state-of-the-art word error rate (WER) and speaker similarity (SIM) on short-form prompts but significantly deteriorate when used with long-form prompts. We propose Localized Attention-Constrained Inference (LACI), an inference-only method to detect TTS errors in near real-time, roll back to the error onset and regenerate with temporary guardrails, adding negligible computational overhead. Using LACI, we improve worst-of-N WER across 10 RNG seeds for Qwen3-TTS-0.6B from 35.2% to 3.4% on prompts longer than 1500 words, even surpassing its short-form reliability of 5.4% on prompts with fewer than 500 words. To demonstrate the efficacy of LACI on voice cloning reliability, we propose a sliding-window version of the SIM metric that we call wSIM. wSIM exposes several novel failure patterns that are not captured by SIM. LACI improves worst-of-N wSIM from 0.01 to 0.47 on 120 seconds of reference audio while reducing the rate of catastrophic generations with WER above 30% from 26% to below 1%
Rongxiang Wang, Berkin Durmus, Aysegul Orhon +2
Argmax, Inc. · University of Virginia · Bilkent University
Open autoregressive neural-codec text-to-speech (TTS) models sound excellent on typical inputs yet suffer stochastic catastrophic failures: on a meaningful fraction of utterances they emit silence, terminate early, or collapse into repetitive or hallucinated content. We show this failure mode is cheap to remove. Under a single format-robust metric (a catastrophic-failure rate via an ASR round-trip), best-of-N ASR self-verification drives failures to near-zero: no observed failures remain by N=2 on a standard corpus (LibriSpeech) and by N=4 on a hard prompt set. This is not an artifact of one model: the reduction replicates across four open codec-TTS systems and three neural codecs (XCodec2, SNAC, Mimi), reaching the near-zero floor by N=2 on three of the four. We then make the fix free at inference time by distilling the self-verified behaviour into the model, which recovers much of the robustness in single-shot decoding, closing ~52-58% of the failure mass on hard inputs at no test-time cost. The distillation gain concentrates where it is needed (hard inputs); on already-reliable prose there is no headroom and no detectable change. A controlled comparison adds a clean negative: offline direct preference optimization (DPO/IPO) does not beat plain supervised distillation, and an online iterative variant is promising but not statistically separable at our evaluation size. We report honestly the one model that resists (a larger Llasa where scale did not obviously help) and a rare-word capability ceiling that no self-distillation method overcomes