Open navigation

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

Qwen3 ASR Flash Realtime

Alibaba · Streaming · #6 of 36 streaming by English WER · alibaba-qwen3-asr-flash-realtime-stream

English WER

4.05%

95% CI 3.76% to 4.36%

TTFS p50 / p95

363 ms

p95 507 ms

$ / 1k audio min

$6.17

list $0.0054/min

Δ Severe

+7.0 pts

3.04% → 10.06% on 100 clips

Accuracy by dataset

English index4.05%3.76% – 4.36%
pipecat (voice-agent turns)1.81%1.61% – 2.04%
primock57 (doctor–patient)6.28%5.73% – 6.87%
Multilingual (de/fr/es/it pooled)16.56%14.78% – 18.59%
VoxPopuli English6.29%5.34% – 7.23%
VoxPopuli German9.78%8.35% – 11.35%
VoxPopuli French14.41%11.03% – 18.34%
VoxPopuli Spanish10.11%7.87% – 12.64%
VoxPopuli Italian29.84%24.72% – 35.27%

English substitutions 1.57%, deletions 1.87%, insertions 0.61% · raw (un-normalized) English WER 19.00% · hallucination 3.90% of clips · empty 0.00% · VoxPopuli CER 11.38%

Streaming latency

p50p90p95p99max
TTFS363 ms465 ms507 ms593 ms5.01 s
TTFT285 ms575 ms594 ms622 ms896 ms
TTFB595 ms615 ms632 ms888 ms1.56 s
Connection setup803 ms826 ms829 ms868 ms1.11 s

English WER@500 ms 4.58% (final 4.05%) · finals before t0 0.0% · fallback finals 0.0% · partial words later rewritten 8.9%

Robustness · same 100 clips

MixWERΔ pts (95% CI)WorseRank
Clean3.04%——#38
Light3.21%+0.2 (−0.6 to +0.9)14/100#28
Moderate9.18%+6.1 (+3.5 to +9.2)37/100#47
Severe10.06%+7.0 (+4.1 to +10.5)40/100#46
Crosstalk10.58%+7.5 (+4.8 to +10.6)46/100#23

Bleed 1.06% of background-talker words · ranks are out of 81 on the subset.

Configuration and pricing

Languages
en, de, fr, es, it
Finalize
input_audio_buffer.commit; ack = first ...transcription.completed after it
Config notes
Manual mode (turn_detection null): only our input_audio_buffer.commit at the VAD speech end produces the final.
Rate
$0.0054/min pay-as-you-go · billed on audio time · 1 s increments
Effective
$6.17 per 1k audio min on this run · clean run cost $2.61
Pricing notes
36,000 s free quota for 90 days after activation; rate shown is the list rate. Docs advise <=60 s of continuous audio per commit in Manual mode.
Source
https://www.alibabacloud.com/help/en/model-studio/model-pricing · checked 2026-10-19
Clip outcomes
ok 2,999