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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
DE

Flux Multilingual

Deepgram · Streaming · #33 of 36 streaming by English WER · deepgram-flux-multi-stream

English WER

7.25%

95% CI 6.72% to 7.88%

TTFS p50 / p95

98 ms

p95 218 ms

$ / 1k audio min

$8.92

list $0.0078/min

Δ Severe

+14.0 pts

5.89% → 19.91% on 100 clips

Accuracy by dataset

English index7.25%6.72% – 7.88%
pipecat (voice-agent turns)4.69%4.26% – 5.14%
primock57 (doctor–patient)9.82%8.74% – 10.92%
Multilingual (de/fr/es/it pooled)14.59%13.56% – 15.77%
VoxPopuli English9.24%8.23% – 10.32%
VoxPopuli German14.53%13.09% – 16.05%
VoxPopuli French12.97%11.14% – 15.06%
VoxPopuli Spanish9.70%8.18% – 11.42%
VoxPopuli Italian21.33%18.71% – 24.04%

English substitutions 4.01%, deletions 2.35%, insertions 0.89% · raw (un-normalized) English WER 21.88% · hallucination 2.74% of clips · empty 0.20% · VoxPopuli CER 8.97%

Streaming latency

p50p90p95p99max
TTFS98 ms177 ms218 ms223 ms880 ms
TTFT457 ms928 ms1.18 s1.85 s5.74 s
TTFB219 ms298 ms299 ms300 ms307 ms
Connection setup120 ms267 ms276 ms295 ms566 ms

English WER@500 ms 7.26% (final 7.25%) · finals before t0 6.6% · fallback finals 6.8% · partial words later rewritten 5.5%

Robustness · same 100 clips

MixWERΔ pts (95% CI)WorseRank
Clean5.89%——#77
Light8.09%+2.2 (+1.3 to +3.2)31/100#78
Moderate16.11%+10.2 (+7.5 to +13.6)64/100#77
Severe19.91%+14.0 (+10.4 to +17.8)64/100#78
Crosstalk22.72%+16.8 (+13.6 to +20.1)80/100#62

Bleed 4.86% of background-talker words · ranks are out of 81 on the subset.

Configuration and pricing

Languages
en, de, fr, es, it
Finalize
forced endpoint
Rate
$0.0078/min pay-as-you-go · billed on audio time · 1 s increments
Effective
$8.92 per 1k audio min on this run · clean run cost $3.77
Source
https://deepgram.com/pricing · checked 2026-10-19
Clip outcomes
ok 2,999