Evidence · first-party tested/Best AI Tools for Accurate Speech-to-Text on Hard Audio

Completes transcription as a single POST with the response returned inline and no operator intervention; the run trace notes that per-call timings were not instrumented.

✓ Worked🧾 artifact-verifiedinput + output shownTest date not recordedGroqCloud →

What was measured

Automation level

How many API steps or calls the workflow requires, and whether it completes without operator input.

context, not decisivecapability

How many API steps or whether operator input is needed affects convenience and workflow, but not whether the engine transcribes accurately once run. (3 of 3 judges)

What was given, what came back

Test input: Medical anatomy narration with dense jargon · audio · group: speech-to-text-benchmark

Input — what we sent

Input file 2 — as supplied

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Medical anatomy narration with dense jargon

A long narrated excerpt from Henry Gray's Anatomy of the Human Body containing dense medical terminology and accented articulation. It was used to test lexical accuracy on domain-specific jargon and spelling of technical terms.

Why this input is hard

· domain-specific vocabulary recognition
· medical term spelling accuracy
· accented speech robustness
· phoneme-to-grapheme precision
· long-form audio handling

Output — unretouched

Also checked on this input — same tool, 2 other criteria

Export✓ WorkedReturns a verbose JSON transcript payload with task, language, duration, segments, word timestamps, confidence data, and no speaker labels; the payload depth is 2/3 with 206 timed tokens.Output quality✓ WorkedDelivers high lexical accuracy on dense medical narration: WER 3.15% with 58 substitutions, 13 deletions, and 15 insertions over 2728 reference words, returning 2730 hypothesis words.

Provenance

Observation: 7ca3e91d-1ecd-4090-b774-95b3fdcace36
Evidence run: 469de0c2-d727-4f8f-a60e-e3a5bf8e8588
Study: Transcribe Audio Accurately — Speech-to-Text Engine Benchmark
Research task: 86baxegpu
Tested at: not recorded
Source: first-party
Evidence state: verified
Proof shown: input + output shown
Cost / latency: not captured
Repeat run: not captured
Tester: not captured

The last three rows are honest blanks, not placeholders — our capture has no field for them yet.

Query this

get_evidence({
  tool: "groqcloud",
  scenario: "speech-to-text-benchmark"
})

MCP · mcp.aidemos.com/api/mcp

JSON · ai.aidemos.com/v1/observations/7ca3e91d-1ecd-4090-b774-95b3fdcace36

Free with attribution.

Same input, same check — 1 other tool

measured on Automation level

Google Cloud Speech-to-Text◐ MixedThe recorded workflow submits the audio as a POST to the v2 endpoint and reaches a scored result without operator input, but the trace explicitly says per-call timings were not instrumented, so no measured call count is claimed.

This evidence is published in

Best AI Tools for Accurate Speech-to-Text on Hard Audio →

Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com