High-accuracy speech data for AI
Audio Transcription
Accurate speech-to-text with speaker diarization, timestamping, and linguistic tagging for voice AI and ASR training.
Overview
What is Audio Transcription?
Training speech recognition and voice AI models requires massive amounts of accurately transcribed audio data with speaker diarization, domain-specific terminology, and precise timestamps.
Why it matters: Automatic transcription systems still struggle with accents, domain jargon, and multi-speaker audio. Human transcription remains the gold standard for high-quality voice AI training data.
Speaker Diarization
Multi-speaker identification with accurate start/end timestamps
Timestamping
Word-level or sentence-level timestamps for alignment tasks
30+ Languages
Native-speaking transcribers for dialects and accents
WER Monitoring
Word error rate tracked and reported per batch
Workflow
How We Do It
01
Audio Assessment
Assess audio quality, language, domain complexity, and speaker count.
02
Transcription
Expert transcribers produce verbatim or clean transcripts with speaker labels and timestamps.
03
Diarization
Speaker diarization separates and labels multiple speakers with start/end timestamps.
04
Linguistic Tagging
Optional entity tagging, sentiment labels, and intent markers for enriched NLP datasets.
05
QA & Validation
Accuracy verified by a second reviewer with automated WER checks.
Case Study
Voice AI Startup
Voice AI Startup
Transcribe 10,000 hours of call center audio in 5 languages
Solution
Specialist transcribers with domain glossaries and automated QA checkpoints
Results
10K
Audio hours processed
98.8%
Accuracy rate
5
Languages covered
Ready to Get Started with Audio Transcription?
Tell us about your project and we'll scope a pilot within 48 hours.