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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

← All models

Muse Voice Transcribe 1.0

Meta · Streaming · #31 of 47 streaming by English WER · meta-muse-voice-transcribe-1-0-stream

English WER

5.29%

95% CI 4.95% to 5.68%

TTFS p50 / p95

152 ms

p95 196 ms

$ / 1k audio min

$3.25

list $0.003/min

Δ Severe

+6.9 pts

4.29% → 11.18% on 100 clips

Accuracy by dataset

English index5.29%4.95% – 5.68%
pipecat (voice-agent turns)3.01%2.71% – 3.31%
primock57 (doctor–patient)7.56%6.87% – 8.26%
Multilingual (de/fr/es/it pooled)14.19%13.14% – 15.34%
VoxPopuli English9.42%8.37% – 10.51%
VoxPopuli German13.09%11.69% – 14.59%
VoxPopuli French13.95%11.99% – 16.04%
VoxPopuli Spanish9.12%7.47% – 10.91%
VoxPopuli Italian20.51%17.90% – 23.10%

English substitutions 2.21%, deletions 1.72%, insertions 1.35% · raw (un-normalized) English WER 23.25% · hallucination 3.00% of clips · empty 0.07% · VoxPopuli CER 9.11%

Streaming latency

p50p90p95p99max
TTFS152 ms185 ms196 ms216 ms359 ms
TTFT830 ms1.16 s1.32 s1.67 s6.37 s
TTFB1.23 s1.91 s2.27 s3.62 s6.85 s
Connection setup704 ms976 ms1.13 s1.61 s3.32 s

English WER@500 ms 5.29% (final 5.29%) · finals before t0 0.0% · fallback finals 0.0% · partial words later rewritten 0.0%

Robustness · same 100 clips

MixWERΔ pts (95% CI)WorseRank
Clean4.29%——#79
Light4.61%+0.3 (−0.2 to +0.9)15/100#73
Moderate9.78%+5.5 (+3.8 to +7.2)50/100#65
Severe11.18%+6.9 (+4.6 to +9.5)57/100#66
Crosstalk14.70%+10.4 (+8.2 to +12.8)73/100#55

Bleed 4.91% of background-talker words · ranks are out of 104 on the subset.

Configuration and pricing

Languages
en, de, fr, es, it
Finalize
endStream (ends input); ack = transcript final=true
Config notes
PUSH_TO_TALK: the clip is one turn; endStream releases a single transcript with final=true.
Rate
$0.003/min pay-as-you-go · billed on audio time
Effective
$3.25 per 1k audio min on this run · clean run cost $1.38
Pricing notes
Billed on audio processed, rounded down to whole seconds; failed and 429 requests not billed.
Source
https://dev.meta.ai/docs/speech-to-text · checked 2026-10-19
Clip outcomes
ok 2,999