AI Speech and Voice
Distinguish transcription from speech generation, prepare audio tasks and evaluate intelligibility, errors and consent.
Before you start: Generative AI. Reading time is an estimate; allow extra time for practice.
What you’ll learn
- Distinguish speech recognition, speech synthesis, translation and speaker diarisation.
- Prepare a short recording and check a transcript against what was actually said.
- Turn an approved script into reviewed synthetic narration with clear disclosure.
- Choose privacy, consent and accessibility checks appropriate to the audio workflow.
On this page
Separate listening from speaking
A library needs an audio announcement for its fictional seed exchange. The approved message says, “The seed exchange opens at ten fifteen in Room B. Bring one labelled packet.” You may want to transcribe an organiser's recording, improve the written script and generate a clear narration. Those are separate tasks with separate error checks. A good synthetic voice cannot repair a wrong time in the script.
Automatic speech recognition, or ASR, maps recorded speech to text. Text-to-speech, or TTS, maps written text to generated audio. Speech translation changes the language as well as representing content. Speaker diarisation labels who spoke when, often with generic speaker IDs; it does not necessarily identify a real person. A product can combine these abilities, but the label “voice AI” does not tell you which operation occurred.
Choose the direction before choosing a tool. If you already have an approved script, you do not need to record it just to transcribe it again. If you need an accurate record of an existing meeting, generating a polished paraphrase is a different deliverable from transcribing the original words. State which output you need and what may be changed.
Understand how sound becomes a transcript
A recording is a sequence of audio samples over time. A recognition system processes that signal into representations useful for predicting text, using learned relationships between acoustic patterns and language. Different systems use different architectures. The Whisper paper is one documented example of a neural encoder-decoder approach trained on audio and text; it is not a description of every current transcription product.
Speech is not a clean sequence of written words. Sounds overlap, speakers hesitate, names can be unfamiliar and background noise can hide a syllable. A model uses context to choose plausible text, which helps with ordinary language but can also produce a plausible wrong word. A polished sentence is therefore not proof that every word was audible.
For the library recording, “ten fifteen” and “ten fifty” would have very different practical meanings. The transcript must be compared with the audio at that point. If the sound is genuinely unclear, mark the uncertainty and ask the speaker or source owner to confirm it. Do not silently resolve a consequential ambiguity from what seems more likely.
Prepare the input and permission together
For practice, record your own voice reading the fictional announcement in a quiet space, or use an authorised recording. Explain the intended use to any other speaker before capturing or uploading their voice. A recording can include private conversation, background names, location clues and other people who were not the intended subject. Trim unnecessary material and review the file before sharing it.
Keep the microphone close enough for clear speech without clipping or rubbing noise. Listen to the beginning, middle and end. Check the correct file, language and relevant segment instead of uploading a folder of recordings. If a tool accepts vocabulary hints, supply genuine terms such as the room label; hints should aid recognition, not force the transcript to match a preferred story.
Review the service's current retention and data-use settings for the actual account. A local-looking interface may still send audio to a remote service. Conversely, an on-device option has its own storage and access considerations. The relevant question is where the audio and resulting transcript are processed and retained, not simply whether the product is called private.
Review words, timestamps and speakers independently
Ask for a faithful transcript with uncertain passages marked. Then listen while reading it. Check names, times, numbers, negations and short words that change meaning. A transcript saying “bring a labelled packet” differs from “do not bring a labelled packet”, even if nearly every other word matches. Overall fluency can hide the most important error.
Timestamps and speaker labels are additional outputs, not automatic guarantees of correctness. Check whether a timestamp identifies the start of a word, a segment or a broad interval. If two voices overlap, a speaker boundary may be wrong even when the recognised words are right. Do not attach a real identity to “Speaker 1” without independent evidence.
Keep a faithful transcript separate from an edited announcement. Removing hesitations and shortening wording can make a script easier to hear, but those edits change its status from a record of speech to a new text. Retain the source and record substantive corrections so a reviewer can see which version was approved for narration.
Task: transcribe the recording I provide, without summarising it.
Context: a fictional library seed-exchange announcement.
Expected vocabulary: Room B; labelled packet.
Mark unclear words rather than guessing.
If timestamps are supported, identify the unclear intervals.
Keep speaker labels generic unless I provide verified identities.
After the transcript, list uncertain times, numbers and room labels.
If the recording is unavailable or unreadable, say so explicitly.Attach only a recording you are authorised to use. This prompt does not imply that an audio file has already been supplied, that timestamps are always supported or that a transcription was performed.
Generate speech from an approved script
TTS systems generate a speech signal conditioned on text and supported voice or style controls. In a documented example such as Tacotron 2, one network predicts an acoustic representation and a vocoder produces a waveform. Other systems use different representations or combine stages. The common practical point is that text meaning and audio delivery both need review.
Prepare a script that reads naturally aloud. Write ambiguous numbers as intended words, expand an abbreviation on first use and add punctuation that supports phrasing. For the seed exchange, “ten fifteen” is clearer for this script than relying on how a tool interprets “10:15”. A pronunciation guide can help with a name, but it must be checked in the actual voice output.
Choose a built-in or appropriately authorised voice rather than imitating a person without permission. Listen for pronunciation, missing words, pacing, unwanted emphasis and sentence endings. A synthetic voice can sound natural while saying the wrong thing or placing emphasis that changes the message. Review every final clip, including any regenerated sentence.
Account for timing in live voice systems
A live assistant adds another loop: detect speech, process it, decide what to say and begin playback. Some systems connect recognition and synthesis as separate stages; others support more direct speech interaction. Either way, partial recognition can change while the person continues speaking, and the assistant may need to stop playback when interrupted.
Distinguish a provisional transcript from a final accepted instruction. “Book two—actually, make that one” should not become two independent bookings. The application needs appropriate turn handling and confirmation for consequential actions. A model's audio response saying “done” is not evidence that an external action succeeded.
For our announcement, an offline reviewed file is simpler than a live agent because there is no need to interpret interruptions or execute actions. Choose the smaller workflow that meets the purpose. Streaming can reduce perceived delay, but it does not remove the need to validate content, manage errors or protect the microphone input.
Disclose and provide another way to access the message
Tell listeners when a voice is synthetic in a context where they could otherwise mistake it for a real speaker. OpenAI's TTS documentation explicitly requires disclosure for its generated voices; consult the rules for the service you actually use. A label about origin should not replace accurate content or imply that the audio is an approved statement by someone it resembles.
Provide the checked script as a transcript. If the announcement accompanies video, captions should include meaningful speech and sound, with timing reviewed against the final media. Keep background music low enough that it does not obscure the announcement. Test the exported file on the device or player where people will hear it, not only through the editor's headphones.
The final review should verify the time, room, required item, intelligibility and synthetic-voice disclosure. This establishes the quality of one announcement. It does not prove that the same recogniser understands every accent or that the same voice pronounces every name correctly. Evaluate a wider range of representative speakers and content before extending the workflow.
Try it yourself
Use your own authorised recording of the fictional announcement, or work from a written simulated transcript that changes “ten fifteen” to “ten fifty”. Identify the consequential mismatch, produce an approved script and write a listening checklist for a synthetic narration.
Show hint
Compare the time with the recording or declared source instead of accepting fluent text. Keep faithful transcription separate from editing. If you generate audio, listen to the complete export and check the room, time and packet instruction; supply a transcript and a clear synthetic-voice notice. A written simulation tests review reasoning, not a recogniser’s measured performance.
Check your understanding
5 questions to revisit the ideas. No score is saved, and you can retry. This is separate from Skill Quiz; it does not award XP or unlock guides.
Sources & further reading
Primary references for the concepts in this guide. Examples and exercises are illustrative; verify provider-specific behaviour against the version you use.