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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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SP

Speechmatics Enhanced RT

Speechmatics · Streaming · #16 of 36 streaming by English WER · speechmatics-enhanced-stream

English WER

4.61%

95% CI 4.28% to 4.92%

TTFS p50 / p95

163 ms

p95 678 ms

$ / 1k audio min

$15.2

list $0.013333/min

Δ Severe

+4.9 pts

2.64% → 7.53% on 100 clips

Accuracy by dataset

English index4.61%4.28% – 4.92%
pipecat (voice-agent turns)2.39%2.15% – 2.68%
primock57 (doctor–patient)6.82%6.19% – 7.45%
Multilingual (de/fr/es/it pooled)12.53%11.46% – 13.66%
VoxPopuli English7.20%6.20% – 8.15%
VoxPopuli German11.98%10.63% – 13.56%
VoxPopuli French10.91%9.11% – 12.83%
VoxPopuli Spanish8.02%6.53% – 9.71%
VoxPopuli Italian19.17%16.57% – 21.87%

English substitutions 2.01%, deletions 1.90%, insertions 0.70% · raw (un-normalized) English WER 19.91% · hallucination 2.64% of clips · empty 0.03% · VoxPopuli CER 8.66%

Streaming latency

p50p90p95p99max
TTFS163 ms537 ms678 ms792 ms944 ms
TTFT673 ms1.04 s1.17 s1.81 s6.76 s
TTFB0 ms0 ms0 ms1 ms8 ms
Connection setup110 ms134 ms151 ms184 ms220 ms

English WER@500 ms 4.61% (final 4.61%) · finals before t0 0.0% · fallback finals 0.0% · partial words later rewritten 17.8%

Robustness · same 100 clips

MixWERΔ pts (95% CI)WorseRank
Clean2.64%——#24
Light3.13%+0.5 (+0.0 to +1.0)12/100#27
Moderate7.93%+5.3 (+3.2 to +7.6)40/100#39
Severe7.53%+4.9 (+3.2 to +7.0)39/100#22
Crosstalk12.78%+10.1 (+8.1 to +12.3)64/100#35

Bleed 5.88% of background-talker words · ranks are out of 81 on the subset.

Configuration and pricing

Languages
en, de, fr, es, it
Finalize
ForceEndOfUtterance, ack = EndOfUtterance with forced=true
Rate
$0.013333/min pay-as-you-go · billed on audio time · 1 s increments
Effective
$15.2 per 1k audio min on this run · clean run cost $6.45
Pricing notes
Subscriptions up to 25% off, model-training opt-in 33% off ($0.54/hr); $100 free credits on sign-up.
Source
https://www.speechmatics.com/pricing · checked 2026-10-19
Clip outcomes
ok 2,999