The automated conversion of spoken dialogue in synchronous or asynchronous meetings into structured text, combining automatic speech recognition with speaker diarisation, punctuation restoration, and optionally action-item extraction. AI-powered meeting transcription systems enable searchable records, accessibility, and downstream summarisation workflows.
Semantic Classification
Content
Meeting transcription systems combine automatic speech recognition (ASR) with speaker diarisation to produce speaker-attributed transcripts of multi-party conversations. Modern ASR engines based on transformer architectures (e.g., Whisper) achieve near-human word error rates in clean acoustic conditions, while diarisation clusters audio segments by speaker identity using voice embeddings.
Post-processing stages add punctuation restoration, paragraph segmentation, and optionally named-entity extraction, sentiment analysis, and action-item detection powered by large language models. The resulting structured transcript supports downstream use cases including meeting summaries, searchable knowledge archives, accessibility compliance, and CRM integration, making meeting transcription a foundational enterprise AI application.