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Working paper / 2026

MISTT

Multi-condition Impairment Speech-to-Text Bench

Abstract

Multi-condition Impairment Speech-to-Text Bench (MISTT) measures the speech-to-text models that voice agents depend on. Every model hears the same 2,999 frozen clips of voice-agent calls, doctor–patient consultations and five-language parliamentary speech, plus 400 degraded copies of 100 of them: phone lines, low-bitrate codecs, packet loss, background noise and an intelligible background talker. Streaming models are timed the way an agent waits on them, from the forced end of speech to the final transcript. Accuracy carries bootstrap confidence intervals, and the degraded copies are paired with their clean originals, so robustness is measured on the same words.

v0 · October 2026

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Ink Preview

Cartesia · Streaming · #5 of 36 streaming by English WER · cartesia-ink-preview-stream

English WER

4.03%

95% CI 3.70% to 4.39%

TTFS p50 / p95

72 ms

p95 98 ms

$ / 1k audio min

$7.04

list $0.0065/min

Δ Severe

+5.8 pts

2.68% → 8.53% on 100 clips

Accuracy by dataset

English index4.03%3.70% – 4.39%
pipecat (voice-agent turns)2.11%1.88% – 2.38%
primock57 (doctor–patient)5.94%5.29% – 6.57%
Multilingual (de/fr/es/it pooled)9.78%8.57% – 11.09%
VoxPopuli English8.14%7.11% – 9.19%
VoxPopuli French11.65%9.79% – 13.55%
VoxPopuli Spanish8.17%6.67% – 9.88%

English substitutions 2.01%, deletions 1.30%, insertions 0.71% · raw (un-normalized) English WER 16.51% · hallucination 1.08% of clips · empty 0.08% · VoxPopuli CER 6.01%

Streaming latency

p50p90p95p99max
TTFS72 ms91 ms98 ms109 ms146 ms
TTFT1.11 s1.40 s1.53 s1.82 s6.56 s
TTFB1.61 s2.25 s2.58 s3.86 s7.06 s
Connection setup48 ms51 ms53 ms62 ms751 ms

English WER@500 ms 4.03% (final 4.03%) · finals before t0 0.0% · fallback finals 0.0% · partial words later rewritten 6.2%

Robustness · same 100 clips

MixWERΔ pts (95% CI)WorseRank
Clean2.68%——#27
Light3.36%+0.7 (+0.0 to +1.3)12/100#33
Moderate7.61%+4.9 (+3.3 to +6.9)39/100#34
Severe8.53%+5.8 (+3.9 to +8.3)41/100#30
Crosstalk9.98%+7.3 (+5.2 to +9.5)47/100#20

Bleed 3.43% of background-talker words · ranks are out of 81 on the subset.

Configuration and pricing

Languages
en, fr, es
Finalize
text "finalize", ack = flush_done
Config notes
Cartesia suggests ~100 ms chunks but accepts any size; 20 ms keeps parity with Nova-3 / ElevenLabs.
Rate
$0.0065/min pay-as-you-go (list $0.0117) · billed on audio time
Effective
$7.04 per 1k audio min on this run · clean run cost $2.48
Pricing notes
ink-preview not on pricing pages (docs list ink-2 at 3 credits/s on both realtime endpoints); assumed same as ink-2, unverified. Credit-based subscription, no pure PAYG.
Source
https://cartesia.ai/pricing · checked 2026-10-19
Clip outcomes
unsupported 400 · ok 2,599