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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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AS

U3.5 Pro Streaming (max accuracy)

AssemblyAI · Streaming · #2 of 36 streaming by English WER · assemblyai-universal-3-5-pro-max-accuracy-stream

English WER

3.93%

95% CI 3.60% to 4.30%

TTFS p50 / p95

173 ms

p95 310 ms

$ / 1k audio min

$7.46

list $0.0075/min

Δ Severe

+3.6 pts

2.12% → 5.77% on 100 clips

Accuracy by dataset

English index3.93%3.60% – 4.30%
pipecat (voice-agent turns)1.86%1.63% – 2.10%
primock57 (doctor–patient)5.99%5.35% – 6.63%
Multilingual (de/fr/es/it pooled)12.33%11.24% – 13.43%
VoxPopuli English6.67%5.69% – 7.65%
VoxPopuli German10.62%9.20% – 12.05%
VoxPopuli French11.08%9.20% – 13.06%
VoxPopuli Spanish7.99%6.47% – 9.71%
VoxPopuli Italian19.20%16.63% – 21.84%

English substitutions 1.50%, deletions 1.74%, insertions 0.68% · raw (un-normalized) English WER 17.42% · hallucination 2.64% of clips · empty 0.00% · VoxPopuli CER 8.55%

Streaming latency

p50p90p95p99max
TTFS173 ms275 ms310 ms388 ms589 ms
TTFT605 ms955 ms956 ms1.06 s6.26 s
TTFB955 ms1.26 s1.66 s3.15 s6.76 s
Connection setup53 ms62 ms70 ms123 ms360 ms

English WER@500 ms 3.96% (final 3.93%) · finals before t0 0.0% · fallback finals 0.0% · partial words later rewritten 17.8%

Robustness · same 100 clips

MixWERΔ pts (95% CI)WorseRank
Clean2.12%——#5
Light2.89%+0.8 (+0.1 to +1.6)11/100#16
Moderate4.97%+2.8 (+1.6 to +4.4)28/100#10
Severe5.77%+3.6 (+2.3 to +5.4)36/100#12
Crosstalk5.65%+3.5 (+2.1 to +5.2)34/100#5

Bleed 1.14% of background-talker words · ranks are out of 81 on the subset.

Configuration and pricing

Languages
en, de, fr, es, it
Finalize
ForceEndpoint; ack = first end_of_turn Turn after it
Config notes
20 ms chunks are rejected (3007: 50-1000 ms only), so this model streams 50 ms frames.
Rate
$0.0075/min pay-as-you-go · billed on session time
Effective
$7.46 per 1k audio min on this run · clean run cost $3.16
Pricing notes
Rate card now lists only 3.6 Pro Realtime; 3.5 Pro Realtime was $0.45/hr. Keyterms included on this model (unused). We terminate each session right after the final to avoid idle billing.
Source
https://www.assemblyai.com/pricing · checked 2026-10-19
Clip outcomes
ok 2,999