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

The 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.

◐ Mixed🧾 artifact-verifiedinput + output shownTest date not recordedGoogle Cloud Speech-to-Text →

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◐ MixedReturns a mid-depth transcript payload: payload depth 2/3 with 2750 word-level timed tokens, confidence present, and no speaker labels.Output quality◐ MixedOn dense medical narration, the transcript reaches 13.09% WER with 228 substitutions, 49 deletions, and 80 insertions against 2728 reference words, and it recalls 100.0% of scored jargon terms.

Provenance

Observation: 677302a8-3a2d-4042-9b1e-5bc8ae009635
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: "google-cloud-speech-to-text",
  scenario: "speech-to-text-benchmark"
})

MCP · mcp.aidemos.com/api/mcp

JSON · ai.aidemos.com/v1/observations/677302a8-3a2d-4042-9b1e-5bc8ae009635

Free with attribution.

Same input, same check — 1 other tool

measured on Automation level

GroqCloud✓ WorkedCompletes 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.

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