Transcription

AI concepts
About 1 min read

The process of listening to spoken words in audio or video and converting them directly into text. Historically performed manually by humans, it is now automated using STT technology to convert recordings of meetings, interviews, and lectures into text, serving as the foundation for search and summarization.

Also known as
TranscriptionTranscriptionDictationSpeech-to-Text

Detailed explanation

Transcription refers to the process of converting spoken language into written language. It is a task that has long been performed for minutes of meetings, subtitle production, legal, and medical records, traditionally carried out manually by stenographers or typists repeatedly listening to audio recordings. Recently, STT (Speech-to-Text) engines have automated this process, turning lengthy recordings into draft text within minutes. However, since automated transcription results can contain errors in technical jargon, homophones, or overlapping speech segments, human validation and correction remain necessary in accuracy-critical fields. When there are multiple speakers, it is integrated with speaker diarization to record who said what.

Why It Matters in Tool Selection

Transcription accuracy is the starting point for meeting minutes automation. If the recognition rate is low, the quality of subsequent summarization and search drops, meaning you must first verify recognition performance for Korean honorifics and technical jargon. Additionally, key drivers of actual workflow efficiency include correction screens that allow humans to quickly fix automated drafts, timestamp synchronization, and integration with speaker diarization.

What to Check

  • Does it accurately transcribe Korean honorifics and homophones?
  • Is there a correction screen to quickly fix misrecognized sections along with the audio?
  • Are timestamps and speaker diarization displayed together in the transcription results?
  • Can it export to required formats such as TXT, SRT, and DOCX?

Application Example

Services like ClovaNote or Daglo automatically transcribe entire conversations into text when a meeting recording is uploaded, then organize summaries and keywords based on the result. YouTube's auto-generated captions are also an example of transcribing video audio to provide subtitles for viewers.

Confusing Terms

STT (Speech-to-Text)

The core engine that converts voice to text; transcription refers to the entire workflow that utilizes this technology.

Summarization

A subsequent step of extracting the core points from the fully transcribed text and organizing them into a brief summary.

Related terms

STTSpeaker DiarizationSummarization