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: Overlapping meeting speech with cross-talk · audio · group: speech-to-text-benchmark

Input — what we sent

Input file 2 — as supplied

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Overlapping meeting speech with cross-talk

A long AMI meeting audio file with multiple speakers talking over one another, background room noise, and crosstalk. It was used to test how well an STT system handles noisy multi-speaker conversational audio and speaker separation.

Why this input is hard

· overlapping speech
· background noise robustness
· multi-speaker separation
· speaker diarization accuracy
· 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 5009 word-level timed tokens, confidence present, and no speaker labels.Output quality⚠ StruggledOn overlapping crosstalk, the transcript quality is poor at 43.50% WER, with 633 substitutions, 2614 deletions, and 50 insertions against 7579 reference words, yielding 5015 hypothesis words.

Provenance

Observation: 30c8040c-4369-44fa-a1f9-2c9315d276f5
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/30c8040c-4369-44fa-a1f9-2c9315d276f5

Free with attribution.

Same input, same check — 1 other tool

measured on Automation level

GroqCloud◐ MixedUses a single multipart POST workflow, but this run stops at API rejection and never completes transcription end to end, so the automation is one-step but unsuccessful on this input.

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