Speech-to-text with diarization and timestamps.
Overview
What is audio transcription?
Speech recognition and voice AI need large volumes of accurately transcribed audio, with speaker diarization, domain terminology and precise timestamps.
Automatic transcription still struggles with accents, jargon and multi-speaker audio. Human transcription remains the gold standard for voice AI training data.
Multi-speaker identification with accurate start and end timestamps.
Word-level or sentence-level timestamps for alignment tasks.
Native-speaking transcribers for dialects and accents.
Word error rate tracked and reported per batch.
Workflow
How we do it
- 01Audio assessment
Assess audio quality, language, domain complexity and speaker count.
- 02Transcription
Expert transcribers produce verbatim or clean transcripts with speaker labels.
- 03Diarization
Diarization separates and labels multiple speakers with start and end timestamps.
- 04Linguistic tagging
Optional entity, sentiment and intent markers for enriched NLP datasets.
- 05QA & validation
Accuracy verified by a second reviewer with automated WER checks.
Case study
Transcribe 10,000 hours of call center audio in five languages
Specialist transcribers with domain glossaries and automated QA checkpoints.
Ready to get startedwith audio transcription?
Tell us about your project and we will scope a pilot within 48 hours.