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Entrytion

Converting spoken language into written text - often referred to as speech recognition, transcription or speech-to-text (STT) - is a task that has traditionally been very time-consuming. Interviews, lectures or group discussions had to be laboriously typed up.

Thanks to artificial intelligence (AI), this process can now be largely automated. Modern AI models understand language better and better, recognize different speakers and translate texts correctly. This saves time and resources and opens up new possibilities, e.g. for accessibility or documentation.

Basics

AI-supported speech recognition systems work with large training data sets to convert speech into text. Accents, dialects and background noises are increasingly recognized and filtered.

The technology is often used for live subtitling, automatic logging or dictating texts. The quality depends on the recording quality, the clarity of the speech and the model used.

Areas of application & possible uses

Event documentation: Automatic transcripts of workshops, lectures or panel discussions.
Accessibility: Live subtitles for people with hearing impairments.
Journalism: Transcription of interviews.
Education: Transcripts of lessons or lectures.
Project work: Automatic minutes of team meetings.

Step-by-step procedure

Step 1: Define target and area of application

Should a conversation be recorded live or should a recording be transcribed later?
Should the text be used directly or edited first?

Step 2: Prepare the recording

Check microphone quality.
Minimize background noise.
If possible: clear speaker announcement and clear pronunciation.

Step 3: Formulate a request to the AI

A good prompt for speech-to-text should contain the following elements:

Context of the recording: z.e.g. lecture, interview, discussion.
Languages or dialects: If relevant.
Format request: Should the text be structured (e.g. paragraphs, speaker assignment) or output as continuous text?
Accuracy requirements: Should the AI also include filler words or automatically smooth the text?

Step 4: Check and edit the result

Check speaker assignment.
Check content for completeness and correctness.
Revise stylistically if necessary.

Step 5: Save and use the finished transcription

Insert into documents or presentations.
Use for follow-up, minutes or publications.

Example from practice

Scenario

An organization would like to document a panel discussion with several guests in order to create a summary article for the website.

Prompt for an AI

"Transcribe the attached 60-minute panel discussion in German. Name the speakers, summarize filler words, pay attention to a clean sentence structure and mark applause or laughter in brackets."

Conclusion

Speech-to-text with AI saves time, increases accuracy and makes it much easier to process spoken content. Especially in education, social projects or public relations work, this technology can help to document content in a more accessible and sustainable way.

Further links

Otter.ai Pro	Live transcription for meetings, workshops or interviews - with speaker recognition and keyword search.
Sembly Professional	Creates meeting notes, recognizes action points, exports directly to project management tools.

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