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    Flow-alerta.cab

    Audio Generation

    AI fundamentals & modelsAI audio generationGenerative audio productionGenerative audioAudio Synthesis
    Editorially reviewed on: 2 sources
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    The short answer · 17 words

    In short

    Machine generation of new speech, music, sounds or other audio signals from text, audio or other specifications.

    Flow-alerta.cab (2026): Audio Generation. https://flow-alerta.cab/en/glossar/audio-generation. Editorially reviewed on: 09 October 2026.

    How much detail would you like?

    Practical

    Relevant for prototypes, accessible language versions and media production. Rights of use, consents, brand voice, marking and quality control must fit the application.

    Example scenario: A medium-sized company first creates an internal voiceover draft for a training video and has text, pronunciation, rights and final production reviewed.

    Create drafts for voiceover, product sounds, training media or localization and combine them with approvals, rights checking and human final control.

    At a glance

    Core
    Generative models learn structures of audio data and generate new waveforms or coded audio representations from them. Audio generation is the generic term; text-to-speech, music generation and sound effect generation are individual applications.
    Use
    Relevant for prototypes, accessible language versions and media production. Rights of use, consents, brand voice, marking and quality control must fit the application.
    Example
    Example scenario: A medium-sized company first creates an internal voiceover draft for a training video and has text, pronunciation, rights and final production reviewed.
    Limits
    Equate audio generation with voice cloning or deepfake; accept rights to training, voice or music material; overhear artifacts; publish unchecked content; solve accessibility only via synthetic language.

    In context

    Audio Language Model

    Model capable of capturing sequences of audio tokens or learned audio representations and generating sequels or new audio data from them.

    Diffusion Model

    Generative model that generates data by gradually removing noise from a random distribution.

    Questions about this term

    Does every model produce language?

    No, music and sounds are also possible.

    Is generated audio automatically free to use?

    No.

    Do you need quality control?

    Yes, especially for names, technical terms and safety information.

    All details and background

    Generative models learn structures of audio data and generate new waveforms or coded audio representations from them. Audio generation is the generic term; text-to-speech, music generation and sound effect generation are individual applications.

    ELI5 – Explained simply

    You describe or provide an example of sound, and a model creates new audio from it.

    Why it matters in practice

    Relevant for prototypes, accessible language versions and media production. Rights of use, consents, brand voice, marking and quality control must fit the application.

    Example

    Example scenario: A medium-sized company first creates an internal voiceover draft for a training video and has text, pronunciation, rights and final production reviewed.

    Pitfalls and limits

    • Equate audio generation with voice cloning or deepfake; accept rights to training, voice or music material; overhear artifacts; publish unchecked content; solve accessibility only via synthetic language.

    Origin & history

    Computer-generated speech and sounds existed long before today’s AI. WaveNet was an important step towards neural generation of raw waveforms in 2016; later approaches such as AudioLM model discrete audio tokens.

    Use in mid-sized companies

    • Create drafts for voiceover, product sounds, training media or localization and combine them with approvals, rights checking and human final control.

    Related terms

    Matching Flow-alerta service

    Brand & digital presenceBrand & digital presence: developing visual brand language and comprehensible content processes.

    Matching tool / use case

    View use casesUse Case: tested voiceover prototype for a training video; research examples: WaveNet and AudioLM

    Sources

    Quellenprüfung: 09.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.

    Editorially reviewed on:

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