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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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MA

Azure Speech Realtime

Microsoft Azure · Streaming · #23 of 36 streaming by English WER · azure-speech-realtime-stream

English WER

5.22%

95% CI 4.84% to 5.62%

TTFS p50 / p95

155 ms

p95 386 ms

$ / 1k audio min

$19.1

list $0.016667/min

Δ Severe

+7.6 pts

3.49% → 11.06% on 100 clips

Accuracy by dataset

English index5.22%4.84% – 5.62%
pipecat (voice-agent turns)3.09%2.79% – 3.40%
primock57 (doctor–patient)7.36%6.62% – 8.07%
Multilingual (de/fr/es/it pooled)14.00%12.98% – 15.10%
VoxPopuli English8.14%7.08% – 9.19%
VoxPopuli German12.87%11.45% – 14.24%
VoxPopuli French13.13%11.26% – 15.09%
VoxPopuli Spanish9.28%7.76% – 11.04%
VoxPopuli Italian20.59%18.08% – 23.24%

English substitutions 2.71%, deletions 1.55%, insertions 0.96% · raw (un-normalized) English WER 19.75% · hallucination 2.37% of clips · empty 0.07% · VoxPopuli CER 9.09%

Streaming latency

p50p90p95p99max
TTFS155 ms283 ms386 ms595 ms1.11 s
TTFT1.01 s1.52 s1.75 s2.42 s11.40 s
TTFB37 ms38 ms38 ms43 ms76 ms
Connection setup118 ms130 ms146 ms185 ms687 ms

English WER@500 ms 5.22% (final 5.22%) · finals before t0 0.0% · fallback finals 0.0% · partial words later rewritten 19.3%

Robustness · same 100 clips

MixWERΔ pts (95% CI)WorseRank
Clean3.49%——#47
Light4.13%+0.6 (−0.1 to +1.4)14/100#50
Moderate8.21%+4.7 (+3.1 to +6.5)45/100#41
Severe11.06%+7.6 (+5.5 to +9.5)59/100#53
Crosstalk10.94%+7.5 (+5.7 to +9.2)61/100#27

Bleed 3.17% of background-talker words · ranks are out of 81 on the subset.

Configuration and pricing

Languages
en, de, fr, es, it
Finalize
empty audio message = end of audio; ack = turn.end (no audio accepted after it)
Config notes
Speech SDK 1.31.0 wire protocol (what AA benchmarked); conversation mode, display-form text.
Rate
$0.016667/min pay-as-you-go · billed on audio time · 1 s increments
Effective
$19.1 per 1k audio min on this run · clean run cost $8.07
Pricing notes
Rate from the Azure Retail Prices API (meter 'S1 Speech To Text', westus3); the pricing page renders prices client-side.
Source
https://azure.microsoft.com/pricing/details/cognitive-services/speech-services/ · checked 2026-10-19
Clip outcomes
ok 2,999