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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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Scribe v3 Live

ElevenLabs · Streaming · #2 of 47 streaming by English WER · elevenlabs-scribe-v3-live-stream

English WER

3.55%

95% CI 3.19% to 3.89%

TTFS p50 / p95

85 ms

p95 120 ms

$ / 1k audio min

$3.98

list $0.003667/min

Δ Severe

+5.0 pts

2.53% → 7.51% on 100 clips

Accuracy by dataset

English index3.55%3.19% – 3.89%
pipecat (voice-agent turns)1.79%1.56% – 2.02%
primock57 (doctor–patient)5.30%4.63% – 5.92%
Multilingual (de/fr/es/it pooled)12.84%11.81% – 13.94%
VoxPopuli English7.36%6.32% – 8.39%
VoxPopuli German11.73%10.16% – 13.33%
VoxPopuli French11.31%9.50% – 13.26%
VoxPopuli Spanish8.41%6.97% – 10.16%
VoxPopuli Italian19.68%17.22% – 22.22%

English substitutions 1.73%, deletions 0.86%, insertions 0.96% · raw (un-normalized) English WER 16.40% · hallucination 3.14% of clips · empty 0.03% · VoxPopuli CER 8.63%

Streaming latency

p50p90p95p99max
TTFS85 ms110 ms120 ms160 ms626 ms
TTFT388 ms645 ms689 ms793 ms4.22 s
TTFB707 ms1.66 s1.97 s3.26 s6.16 s
Connection setup95 ms104 ms112 ms302 ms1.99 s

English WER@500 ms 3.51% (final 3.55%) · finals before t0 0.0% · fallback finals 0.0% · partial words later rewritten 11.8%

Robustness · same 100 clips

MixWERΔ pts (95% CI)WorseRank
Clean2.53%——#35
Light2.33%−0.2 (−1.8 to +0.9)10/100#11
Moderate5.91%+3.4 (+1.1 to +5.5)34/100#25
Severe7.51%+5.0 (+2.1 to +7.7)35/100#29
Crosstalk9.24%+6.7 (+3.5 to +10.3)43/100#24

Bleed 2.79% of background-talker words · ranks are out of 104 on the subset.

Configuration and pricing

Languages
en, de, fr, es, it
Finalize
commit: true; ack = next committed_transcript
Config notes
manual commit: only our commit at the shared VAD speech end produces the final (plus auto-commits on very long audio).
Rate
$0.003667/min pay-as-you-go · billed on audio time
Effective
$3.98 per 1k audio min on this run · clean run cost $1.68
Pricing notes
Billed per audio minute; partial-minute rounding not published.
Source
https://elevenlabs.io/pricing/api · checked 2026-11-10
Clip outcomes
ok 2,999