Chapter BAI Voice GenerationPage 7 of 8

AI Voice Generation

Work a full example

A worked project proves the method by showing decisions, failures, corrections, and evidence.

~14 minWorked example

Before you start

Why this matters

Without opening an AI tool, write the acceptance test for this job: produce a clear, consented thirty-second course welcome in synthetic speech. Name one fact that must be exact, one judgment a person must make, and one condition that should stop the workflow. Compare your answer with the professional standard below; the gap is what you should practice.

1Learn the idea

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Project brief

The project is to create a consented welcome clip through script edit, sample audition, full render, listening QA, transcript, and disclosure. The user is adult beginners listening on headphones or phone speakers. Definition of done: the intended action is clear, the candidate uses approved evidence, blocking safety checks pass, and another person can reproduce the key result.

Stage 1: prepare

Create the job card and collect final script, listener, permitted voice identity, pace, emphasis, pauses, pronunciation guide, disclosure, and transcript. Remove or replace prohibited material: never clone a voice without informed, documented permission; protect raw voice samples as biometric-like data and restrict storage, access, and reuse. Add one ordinary case, one boundary case, and one hostile or misleading case. Record unknowns instead of filling them with plausible guesses.

Stage 2: draft

Read this 50-word course welcome in a warm, clear fictional voice at about 135 wpm. Do not imitate any known person. Pause after sentence one; emphasize “try one thing.” Use the approved pronunciation guide for Nguyen. Generate a ten-second sample first and retain a transcript.

The first candidate should be A short sample whose pace, emphasis, pronunciation, and disclosure can be reviewed before rendering the full message. In this worked run, imagine it also exhibits one realistic defect from this set: celebrity imitation; ambiguous consent; pronunciation drift; robotic pacing; artifact breaths; missing disclosure; audio-only delivery without transcript. Do not hide the defect. Mark the exact criterion it violates and decide whether the cause belongs to context, instruction, model capability, or the surrounding process.

Stage 3: repair narrowly

Issue a targeted revision:

Revise only the failed criterion identified below.
Preserve all verified content and the original output contract.
Do not add facts or assets.
Return the corrected artifact plus a one-line change note.
Failed criterion: [paste criterion and evidence]

A narrow repair keeps the review surface understandable. If the model cannot repair without new authoritative information, pause and obtain that information.

Stage 4: verify and release

Now listen without reading, compare every word to the script, check names, clipped endings, breaths, volume, pace, phone-speaker intelligibility, and transcript accuracy. Record pass/fail evidence for each criterion and have the named reviewer make the release decision. A technically convincing voice can still be unethical or deceptive. Permission must cover the intended use, duration, audience, storage, and revocation path. Save limitations in language the audience can understand.

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Retrospective

The durable deliverable is not only the final result. It is a voice production record with consent scope, script version, pronunciation sheet, direction, sample review, QA log, transcript, and disclosure. Write what surprised you, which check found it, what you changed, and which control should become the default. A clean retrospective distinguishes a prompt improvement from a data, tool, or policy change.

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Continue learning · glossary & guides
  • Can the reviewer see the failed first attempt and why the correction was justified?
  • Does the release packet contain evidence, ownership, and known limitations?
  • Reference · Related concept
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