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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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MI

MAI-Transcribe-2 Streaming

Microsoft · Streaming · #1 of 47 streaming by English WER · microsoft-mai-transcribe-2-streaming-stream

English WER

3.43%

95% CI 3.14% to 3.73%

TTFS p50 / p95

102 ms

p95 226 ms

$ / 1k audio min

$1.81

list $0.001667/min

Δ Severe

+2.4 pts

2.18% → 4.60% on 100 clips

Accuracy by dataset

English index3.43%3.14% – 3.73%
pipecat (voice-agent turns)2.24%1.98% – 2.48%
primock57 (doctor–patient)4.63%4.09% – 5.18%
Multilingual (de/fr/es/it pooled)12.78%11.70% – 13.85%
VoxPopuli English7.03%6.06% – 8.01%
VoxPopuli German10.84%9.51% – 12.26%
VoxPopuli French11.83%9.80% – 13.92%
VoxPopuli Spanish8.46%6.96% – 10.18%
VoxPopuli Italian19.55%16.90% – 22.29%

English substitutions 1.52%, deletions 0.95%, insertions 0.96% · raw (un-normalized) English WER 15.45% · hallucination 2.92% of clips · empty 0.00% · VoxPopuli CER 8.58%

Streaming latency

p50p90p95p99max
TTFS102 ms172 ms226 ms330 ms3.06 s
TTFT302 ms405 ms455 ms583 ms2.58 s
TTFB615 ms1.38 s1.47 s2.26 s4.88 s
Connection setup454 ms1.61 s2.07 s2.46 s7.59 s

English WER@500 ms 4.04% (final 3.43%) · finals before t0 0.0% · fallback finals 0.0% · partial words later rewritten 30.6%

Robustness · same 100 clips

MixWERΔ pts (95% CI)WorseRank
Clean2.09%——#8
Light2.64%+0.5 (+0.0 to +1.0)11/100#17
Moderate4.20%+2.0 (+1.1 to +3.1)26/100#9
Severe4.60%+2.4 (+1.2 to +3.6)28/100#4
Crosstalk7.62%+5.4 (+3.3 to +7.8)37/100#15

Bleed 3.34% of background-talker words · ranks are out of 104 on the subset.

Configuration and pricing

Languages
en, de, fr, es, it
Finalize
input_audio_buffer.commit; ack = transcription.completed
Config notes
Foundry /mai/v1/realtime against a MAI-Transcribe-2-Streaming deployment (eastus2); no server VAD, commit = finalize.
Rate
$0.001667/min pay-as-you-go · billed on audio time
Effective
$1.81 per 1k audio min on this run · clean run cost $0.77
Pricing notes
Streaming price not verified; assumed equal to MAI-Transcribe-2's $0.10/hr introductory rate.
Source
https://azure.microsoft.com/pricing/details/ai-foundry-models/microsoft/ · checked 2026-10-28
Clip outcomes
ok 2,980 · provider_error 19