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5.2.3. Custom Speech and Word Error Rate
- Concept: Train speech recognition on your domain and accent
- Purpose: Improve transcription accuracy for specialized content
- Benefit: Accurate transcription of domain vocabulary
Word Error Rate (WER) Components:
| Error Type | Cause | Solution |
|---|---|---|
| Substitution | Wrong word recognized | Add custom vocabulary (names, products) |
| Deletion | Word missed | Address overlapping speakers |
| Insertion | Extra word added | Reduce background noise |
Visual: WER Error Types
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Exam Alert: "High WER with substitution errors" → Add custom product and people names to training data
Key Trade-Offs:
- Custom vs. Generic Models: Custom models improve domain accuracy but require training investment
- Real-time vs. Batch: Real-time provides immediate results but may have lower accuracy; batch allows more processing time
Reflection Question: Your speech transcription has 15% WER with mostly substitution errors for product names. What's your improvement strategy?