Working paper / 2026
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
StepFun · Batch · #25 of 45 batch by English WER · stepfun-stepaudio-2-5-asr-batch
English WER
4.77%
95% CI 4.41% to 5.16%
E2E p50
764 ms
p95 11.54 s · RTFx 3.8×
$ / 1k audio min
$0.37
list $0.000367/min
Δ Severe
+8.7 pts
2.80% → 11.54% on 100 clips
| English index | 4.77% | 4.41% – 5.16% |
| pipecat (voice-agent turns) | 2.16% | 1.92% – 2.42% |
| primock57 (doctor–patient) | 7.38% | 6.70% – 8.00% |
| Multilingual (de/fr/es/it pooled) | 10.77% | 9.32% – 12.27% |
| VoxPopuli English | 7.74% | 6.70% – 8.82% |
| VoxPopuli French | 11.87% | 9.69% – 14.17% |
| VoxPopuli Spanish | 9.82% | 8.17% – 11.64% |
English substitutions 2.05%, deletions 1.52%, insertions 1.20% · raw (un-normalized) English WER 18.43% · hallucination 1.50% of clips · empty 0.04% · VoxPopuli CER 6.77%
| p50 | p90 | p95 | p99 | max | |
|---|---|---|---|---|---|
| End-to-end | 764 ms | 6.01 s | 11.54 s | 21.32 s | 47.65 s |
Speed factor (RTFx) 3.8×
| Mix | WER | Δ pts (95% CI) | Worse | Rank |
|---|---|---|---|---|
| Clean | 2.80% | — | — | #34 |
| Light | 3.33% | +0.5 (+0.0 to +1.1) | 12/100 | #31 |
| Moderate | 9.62% | +6.8 (+4.4 to +9.5) | 44/100 | #50 |
| Severe | 11.54% | +8.7 (+5.3 to +12.7) | 47/100 | #56 |
| Crosstalk | 24.32% | +21.5 (+16.8 to +26.5) | 72/100 | #64 |
Bleed 4.91% of background-talker words · ranks are out of 81 on the subset.
