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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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DE

Nova-3 Streaming

Deepgram · Streaming · #26 of 36 streaming by English WER · deepgram-nova-3-stream

English WER

5.43%

95% CI 5.01% to 5.82%

TTFS p50 / p95

103 ms

p95 170 ms

$ / 1k audio min

$5.49

list $0.0048/min

Δ Severe

+13.5 pts

4.05% → 17.55% on 100 clips

Accuracy by dataset

English index5.43%5.01% – 5.82%
pipecat (voice-agent turns)3.40%3.10% – 3.73%
primock57 (doctor–patient)7.46%6.66% – 8.22%
Multilingual (de/fr/es/it pooled)15.30%14.27% – 16.35%
VoxPopuli English9.55%8.24% – 10.98%
VoxPopuli German15.52%13.99% – 17.02%
VoxPopuli French13.88%11.99% – 15.72%
VoxPopuli Spanish10.05%8.55% – 11.78%
VoxPopuli Italian22.05%19.48% – 24.79%

English substitutions 2.71%, deletions 1.85%, insertions 0.86% · raw (un-normalized) English WER 22.71% · hallucination 2.74% of clips · empty 0.13% · VoxPopuli CER 10.28%

Streaming latency

p50p90p95p99max
TTFS103 ms156 ms170 ms203 ms272 ms
TTFT848 ms1.09 s1.14 s2.00 s7.10 s
TTFB1.06 s1.11 s1.12 s1.17 s1.31 s
Connection setup97 ms247 ms253 ms268 ms364 ms

English WER@500 ms 5.43% (final 5.43%) · finals before t0 0.0% · fallback finals 0.0% · partial words later rewritten 8.6%

Robustness · same 100 clips

MixWERΔ pts (95% CI)WorseRank
Clean4.05%——#62
Light5.41%+1.4 (+0.6 to +2.2)32/100#74
Moderate14.30%+10.3 (+6.4 to +14.7)51/100#72
Severe17.55%+13.5 (+9.3 to +18.2)62/100#74
Crosstalk23.92%+19.9 (+14.5 to +25.4)75/100#63

Bleed 3.38% of background-talker words · ranks are out of 81 on the subset.

Configuration and pricing

Languages
en, de, fr, es, it
Finalize
forced endpoint
Config notes
endpointing=false: only our Finalize at the shared VAD speech end produces the final.
Rate
$0.0048/min pay-as-you-go (list $0.0077) · billed on audio time · 1 s increments
Effective
$5.49 per 1k audio min on this run · clean run cost $2.32
Pricing notes
Streaming rates are a limited-time promotion, no end date published. Single-language codes (de, fr, es, it) billed at Monolingual; Multilingual is language=multi. Inferred from developers.deepgram.com/docs/models-languages-overview, confirm with Deepgram billing.
Source
https://deepgram.com/pricing · checked 2026-10-19
Clip outcomes
ok 2,999