AuraOne / Human Data / Voice
Preparing speech-data, evaluation, rights, review, and program-routing details.
AuraOne / Human Data / Voice
Build and release speech models, dubbing systems, and realtime voice agents with consent records, documented license scope, trained human review, reusable regression sets, and safety evidence.
Programs are scoped by intended use, rights, target languages, model or agent revision, review criteria, and the decision the team needs to make.
Program surfaces
Each program identifies the source audio, applicable rights, reviewer criteria, model or agent revision, and resulting release decision.
Human recordings retain the consent record, speaker context, language or accent coverage, transcript state, and documented license scope for review.
Speech, transcript, speaker, rightsReviewers score naturalness, pronunciation, latency, interruption handling, task completion, and safety against versioned criteria.
Rubric, reviewer, score, rationaleReusable test sets follow voices, languages, agents, and checkpoints so a release can be compared with the last accepted state.
Prompt, audio, checkpoint, comparisonImpersonation, unauthorized cloning, scam scenarios, synthetic speech detection, and consent boundaries stay attached to the review record.
Scenario, policy, finding, dispositionAudio quality is necessary, but the decision also needs rights, task context, reviewer ownership, and model revision.
Input
Work
Output
The same trace applies to training data, model evaluation, agent testing, localization, and safety review.
01
Recruit the speaker or define the evaluation scenario with consent and rights attached.
Speaker, prompt, scenario, consent, license
02
Verify audio, transcript, accent or language fit, and rubric criteria.
Reviewer, rubric version, score, rationale
03
Run the same benchmark against the candidate and accepted baseline.
Checkpoint, regression set, comparison
04
Accept, rework, block, or release with the boundary stated.
Disposition, owner, exception, next run
Coverage design
Programs can combine regional language coverage, realistic environments, interruption patterns, emotional delivery, and code-switching without losing source and reviewer context.
Coverage is scoped by target market, model behavior, and known failure modes rather than a generic language count.
Define coverageCoverage is scoped by target market, model behavior, and known failure modes rather than a generic language count.
Define coverageCoverage is scoped by target market, model behavior, and known failure modes rather than a generic language count.
Define coverageCoverage is scoped by target market, model behavior, and known failure modes rather than a generic language count.
Define coverageCoverage is scoped by target market, model behavior, and known failure modes rather than a generic language count.
Define coverageCoverage is scoped by target market, model behavior, and known failure modes rather than a generic language count.
Define coverageEvidence packet preview
The production record uses the actual source, model revision, reviewer identity, criteria, and approval state.
| Outcome | Work | Evidence | Boundary |
|---|---|---|---|
| Accept a speech dataset | Capture, transcribe, review, adjudicate, and package the approved subset. | Consent, license, speaker context, transcript, QA, and data card. | Scope is limited to the approved languages, scenarios, and uses. |
| Release a voice model | Compare the candidate with the accepted baseline across target coverage. | Scorecards, regressions, failures, reviewer rationale, and approval. | A passing aggregate does not erase a blocked regional or safety cohort. |
| Operate a voice agent | Test task completion, turn-taking, interruption handling, and policy behavior. | Conversation traces, rubric scores, policy findings, and escalation state. | Production monitoring and live-provider controls remain separate operating work. |
