Licensed speech, every accent
Real people, recorded with consent. Every file carries its rights and its speaker context.
Speech, transcript, speaker, rightsAuraOne / Human Data / Voice AI
Every voice your product will ever hear.
Licensed speech across languages, accents, and the conditions your product actually meets.
The accent it misses. The interruption it ignores. The silence that loses the customer.
- Input
- Permissioned speech, scenarios, prompts, and release criteria
- Output
- Reviewed audio, scorecards, regressions, and safety findings
Voice programs
Start from the voice decision the team needs to defend.
Every program names the source audio, the rights that cover it, and the reviewer criteria. The release decision points back to all three.
Human evaluation
Reviewers score how it sounds and whether it listened. Against criteria you set.
Rubric, reviewer, score, rationaleRegression benchmarks
The same test set follows every checkpoint. You see what got worse.
Prompt, audio, checkpoint, comparisonSafety programs
Impersonation, cloning, and scam scenarios. Tested before someone else finds them.
Scenario, policy, finding, dispositionFrom one recording to a release you can defend.
Clean audio is the easy part. The rights and the reviewer are what make it usable.
Input
Speech you are allowed to use.
- Speaker consent and usage scope
- Language, accent, and scenario
- Model, voice, or agent revision under review
Work
Record it. Then review it.
- Transcription and audio-quality review
- Naturalness, pronunciation, and task scoring
- Safety scenarios, overrides, and adjudication
Output
A result you can reuse.
- Accepted speech data and data card
- Evaluation scorecard and reviewer rationale
- Regression bank entry and release recommendation
How a voice program runs
Four steps. The same path whether you are training a model or testing an agent.
01
Source
Recruit the speaker or define the evaluation scenario with consent and rights attached.
Speaker, prompt, scenario, consent, license
02
Review
Verify audio, transcript, accent or language fit, and rubric criteria.
Reviewer, rubric version, score, rationale
03
Compare
Run the same benchmark against the candidate and accepted baseline.
Checkpoint, regression set, comparison
04
Decide
Accept, rework, block, or release with the approved scope stated.
Disposition, owner, exception, next run
Coverage design
Language coverage is designed around actual release risk.
A program can mix regional coverage, noisy rooms, and code-switching. The source and the reviewer stay attached to every file.
English accents and regional variants
Coverage is scoped by target market, model behavior, and known failure modes rather than a generic language count.
Define coverageSpanish and Portuguese variants
Coverage is scoped by target market, model behavior, and known failure modes rather than a generic language count.
Define coverageIndian languages and code-switching
Coverage is scoped by target market, model behavior, and known failure modes rather than a generic language count.
Define coverageArabic regional variants
Coverage is scoped by target market, model behavior, and known failure modes rather than a generic language count.
Define coverageEast and Southeast Asian languages
Coverage is scoped by target market, model behavior, and known failure modes rather than a generic language count.
Define coverageEuropean languages and localization
Coverage is scoped by target market, model behavior, and known failure modes rather than a generic language count.
Define coverageEvaluation workflow
Voice evaluation walkthrough
See how the recording, the criteria, and the decision stay connected.
- Source
- Bilingual conversation set under licensing reviewConsent record and license scope attached
- Revision
- Realtime agent candidate 24Baseline candidate 23
- Review
- Naturalness, latency, and safetyVersioned rubric and reviewer rationale
- Decision
- Hold for accent regression repairOwner and rerun criteria named
What the work lets you decide.
| Outcome | Work | What you receive | Program fit |
|---|---|---|---|
| Accept a speech dataset | Capture it, transcribe it, and package the approved subset. | Consent, license, speaker context, data card. | Best fit: defined languages, scenarios, and acceptance criteria. |
| Release a voice model | Compare the candidate with the accepted baseline across target coverage. | Scorecards, retained regressions, and reviewer rationale. | Regional and safety cohorts remain visible beside the aggregate result. |
| Operate a voice agent | Test whether it finishes the task, takes its turn, and follows policy. | Conversation traces, rubric scores, and escalation state. | Live monitoring and provider controls can be included in the operating plan. |