Is transcription software considered generative AI?

Transcription software is AI, but a plain transcription tool is not clearly generative AI. The difference is between recognizing speech and generating text. Speech recognition is machine learning, so it is AI. Generative AI, in the working definition NIST uses, creates new content. A transcript is derived from a recording and is meant to match what was said, so it sits at the edge of that definition. It becomes clearly generative when a language model rewrites, summarizes or answers questions about the audio. Some apps offer both kinds of output, so the category alone does not settle the question for a given product.

The guide to human vs AI transcription covers accuracy, cost and speed. This page answers only the category question.

Transcription software is AI because its speech recognition is machine learning

Speech recognition has been machine learning for over a decade. In a 2012 review, four research groups wrote that deep neural networks with many hidden layers, trained with new methods, have been shown to outperform older Gaussian mixture models on a variety of speech recognition benchmarks ¹. The authors also say the margin varied by benchmark, so the review shows a direction rather than one fixed score.

Many current engines are built on the same family of models. Encoder-decoder speech models, a common design, implicitly learn a language model from their training data ². So a modern transcription product is AI in the technical sense. For how the recognizer works, see automatic speech recognition.

Generative AI is defined by the new content it produces

NIST's 2024 Generative AI Profile gives a working definition: the class of AI models that emulate the structure and characteristics of input data in order to generate derived synthetic content ³. The profile says this content can include audio and text. NIST quotes this wording from Executive Order 14110 of October 2023, and that order was revoked in January 2025 ⁴. The definition still describes the usual idea of generative AI: models that produce content, not only a record of what was said.

Applied to transcription, the key word is "synthetic." A transcript is derived from the recording, and its job is to match the speech. On that reading, a plain transcript is AI but not clearly generative. NIST does not discuss transcription directly, so this is our interpretation of the definition, not a ruling from NIST.

The same model type can recognize speech or write text, which blurs the line

The architecture is the reason the question is hard. An encoder-decoder model reads audio and writes text one piece at a time, which is also how a language model writes. Whether the output counts as generative therefore depends on what the text is used for, not only on the model design.

The line becomes clear when a language model is applied after recognition. A research benchmark called HyPoradise showed that a large language model can correct words missing from the recognizer's N-best list, because it generates text rather than only choosing among candidates ⁵. This step is called generative error correction, and it is generative by any reading.

Generated text can also contain things the audio does not. In a 2024 study, roughly 1 percent of Whisper audio transcriptions contained entire hallucinated phrases or sentences that did not exist in the audio ⁶. The study tested a 2023 version of the model, so newer versions may differ. Invented text is one more reason to check the output against the recording.

Whether an app is generative depends on what it does after recognition

The same word, "transcription," covers products that do very different things. The table sorts them by what happens to the text after the speech is recognized.

Some apps offer both kinds of output. Superwhisper, for example, documents a mode that skips its language model stage ⁷, which means the same app can produce raw text or model-processed text depending on the setting. So the answer for a product is in its settings and documentation, not in the phrase "AI" on its download page.

What to do if the label matters for your work

1. Read how the app processes text. Look for options named cleanup, rewrite, summary or a model-based mode, and check whether a raw or plain mode exists.
2. Choose raw output for words you must quote. If a transcript will be used as a record or in research, raw text stays closest to the speech. Then check it against the audio.
3. Check the policy's own definition. Some schools and employers define AI more broadly than NIST does. A plain transcription tool may count as AI under their rules even though it is not clearly generative under NIST's wording, so ask which definition applies.
4. Check silences and unclear sections. Recognizers can produce text that is not in the recording ⁶, so review those parts first.

For the accuracy side of the question, see how accurate AI transcription is. For what a transcript costs, see how much transcription costs per minute. For the broader question of whether speech recognition counts as AI, see is speech recognition AI? and the guide on is speech to text AI. The transcription overview lists every guide in this area.