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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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Saaras v4

Sarvam · Streaming · #3 of 36 streaming by English WER · sarvam-saaras-v4-stream

English WER

3.94%

95% CI 3.63% to 4.24%

TTFS p50 / p95

344 ms

p95 413 ms

$ / 1k audio min

$6.52

list $0.00568/min

Δ Severe

+5.0 pts

2.72% → 7.77% on 100 clips

Accuracy by dataset

English index3.94%3.63% – 4.24%
pipecat (voice-agent turns)1.74%1.53% – 1.97%
primock57 (doctor–patient)6.15%5.57% – 6.73%
Multilingual (de/fr/es/it pooled)—
VoxPopuli English7.18%6.19% – 8.13%

English substitutions 1.75%, deletions 1.31%, insertions 0.89% · raw (un-normalized) English WER 17.97% · hallucination 0.23% of clips · empty 0.00% · VoxPopuli CER 4.25%

Streaming latency

p50p90p95p99max
TTFS344 ms396 ms413 ms461 ms773 ms
TTFT1.08 s1.82 s1.95 s2.06 s2.38 s
TTFB1.24 s1.25 s1.26 s1.28 s1.68 s
Connection setup691 ms715 ms725 ms743 ms1.28 s

English WER@500 ms 3.96% (final 3.94%) · finals before t0 0.0% · fallback finals 0.0% · partial words later rewritten 10.2%

Robustness · same 100 clips

MixWERΔ pts (95% CI)WorseRank
Clean2.72%——#30
Light2.93%+0.2 (−0.2 to +0.6)10/100#20
Moderate6.37%+3.6 (+2.3 to +5.3)34/100#21
Severe7.77%+5.0 (+3.1 to +7.4)42/100#23
Crosstalk6.65%+3.9 (+2.6 to +5.3)39/100#11

Bleed 0.76% of background-talker words · ranks are out of 81 on the subset.

Configuration and pricing

Languages
en
Finalize
{"event":"speech_end"}, ack = next transcript.final
Config notes
Manual endpointing: speech_start on open, speech_end is our force-endpoint (flush/VAD don't map to it).
Rate
$0.00568/min pay-as-you-go · billed on audio time · 1 s increments
Effective
$6.52 per 1k audio min on this run · clean run cost $1.81
Pricing notes
Priced in INR only; USD assumes ~₹88/USD. sarvam.ai/api-pricing shows real-time STT ₹0.49/min PAYG (Pro ₹0.45, Growth/Business ₹0.41).
Source
https://docs.sarvam.ai/api/getting-started/pricing · checked 2026-10-19
Clip outcomes
unsupported 799 · ok 2,200