Custom Vocabulary for Technical ASR
Highly accurate transcription (98%+) for specialized, technical audio content.
The Problem
Need to transcribe medical or legal audio where standard ASR models fail due to highly specific, domain-related jargon.
Expected Outcome
Highly accurate transcription (98%+) for specialized, technical audio content.
Tool Chain
Implementation Steps
- 1
Domain Data Collection
Collect a corpus of 10-20 hours of relevant audio and transcripts (e.g., past medical consultations, legal proceedings).
- 2
Custom Language Model Training
Use the custom data to train an augmented ASR model, significantly improving accuracy on domain-specific terms.
IBM Watson Speech to Text - 3
Real-Time Transcription and Testing
Stream the target audio for real-time transcription, testing the new model's accuracy against a control group.
- 4
Feedback Loop
Implement a human-in-the-loop QA step to correct errors and feed them back to the training model.
Alternatives
Similar robust customization, better integration with Azure tools.
Cost Impact: N/A
Cheaper in the long run (self-hosted), but requires significant MLOps expertise.
Cost Impact: -50%
Export Workflow
Coming SoonSoon you’ll export this stack to Zapier, n8n, or a starter repo with presets (env vars, webhooks, rate limits).