Standard STT (speech recognition) models are trained on general corpora, but specialized vocabulary—medical STT, legal STT, and technical STT—often results in high Word Error Rate (WER). In medical dictations, the WER reaches 25–30%, with half the terms requiring post-editing. For lawyers, errors in names and dates can cost a lawsuit. We solve this with a combination of boosting, custom dictionaries, and fine-tuning of Whisper. Our team has 5+ years of experience in NLP and has completed 30+ projects customizing STT.
For example, in a project for a chain of clinics, we reduced WER from 28% to 12% in two weeks using a combination of boosting and post-correction. This saved doctors 40% of transcription time. In another case for a law firm, boosting legal phrases with boost=18 cut WER for judges' names and code articles by three times. Boosting with adaptive phrases is also effective for technical vocabulary (e.g., STM32, REST API).
Improving Recognition of Specialized Vocabulary
The fastest method is Custom Vocabulary / Boosting. It requires no retraining and boosts accuracy on rare terms by 2–3 times. For instance, boosting 15 medical phrases with boost=15 improves their recognition accuracy by 40%. Adaptive phrases can be configured for Google STT, AWS Transcribe, and Azure Speech.
# Google STT — adaptive phrases from google.cloud import speech speech_context = speech.SpeechContext( phrases=[ "atrial fibrillation", "ventricular fibrillation", "atrioventricular block", "ECG", "QRS complex" ], boost=15.0 # range 1 to 20 ) config = speech.RecognitionConfig( speech_contexts=[speech_context], language_code="ru-RU" ) The second method is post-correction using a dictionary with fuzzy matching. It catches phonetic errors without slowing processing.
from fuzzywuzzy import fuzz DOMAIN_TERMS = { "dexamethozone": "dexamethasone", "myocardial infarction": "myocardial infarction", "hypothyroidism": "hypothyroidism", } def correct_medical_terms(text: str, threshold: int = 80) -> str: words = text.split() for i, word in enumerate(words): for wrong, correct in DOMAIN_TERMS.items(): if fuzz.ratio(word.lower(), wrong) >= threshold: words[i] = correct return " ".join(words) Boosting Limitations
Boosting has little effect on synonyms and grammatical constructs. If the base model confuses "extrasystole" and "extrosystole," boosting will fix it. But if it recognizes "atrial fibrillation" as "atrial fibrillation" with an error in the ending—boosting is powerless. Here, post-correction is needed, which replaces whole phrases based on patterns.
Why a Combined Approach Yields Better Results
Boosting is effective for tens of terms but fails with synonyms and grammar. Post-correction fixes phonetics but requires a dictionary. Together they cover each other's weaknesses. The combined approach is 3 times better than boosting alone, reducing WER by 50–80% without the cost of data labeling, according to Microsoft Research. For example, a typical medical client saves $12,000 annually after adaptation.
What's Included in STT Adaptation for Your Domain
We provide a turnkey solution:
- a domain-specific vocabulary (500–5000 terms);
- boosting configuration for cloud STT (Google, AWS, Azure);
- a post-correction pipeline with 95%+ accuracy;
- a WER report before and after adaptation;
- training for operators working with the improved system.
Method Comparison
| Method | Implementation Time | WER Reduction | Data Required | Cost |
|---|---|---|---|---|
| Boosting | 1–2 days | 20–40% | Only list of terms | $500–$1,000 |
| Post-correction | 2–3 days | 10–30% | Dictionary with variants | $1,000–$2,000 |
| Whisper fine-tuning | 2–4 weeks | 50–70% | 10+ hours of dictations | $2,500–$15,000 |
| Combined approach | 3–5 days | 50–80% | Minimal requirements | $1,000–$5,000 |
Clients typically save $3,000–$10,000 per year on transcription costs after adaptation, with ROI in 3–6 months.
Typical WER by Domain
| Domain | Standard Model | After Adaptation |
|---|---|---|
| Medicine | 25–30% | 8–15% |
| Law | 20–25% | 5–10% |
| Technology | 15–20% | 5–8% |
Example Boosting Configuration for AWS Transcribe
{ "VocabularyName": "medical-phrases", "LanguageCode": "ru-RU", "Phrases": ["extrasystole", "atherosclerotic", "endoprosthesis"], "VocabularyFilter": { "VocabularyFilterName": "medical-filter", "VocabularyFilterMethod": "mask" } } Work Process
- Corpus analysis — we identify rare terms and typical recognition errors. This stage produces the initial vocabulary.
- Boosting configuration — we set up adaptive phrases for cloud STT. We optimize boost parameters for each service (Google, AWS, Azure).
- Post-correction — we create a domain-term dictionary with fuzzy matching, accounting for phonetic variants and typos.
- Testing — we measure WER on a representative sample and improve iteratively. Usually 2–3 iterations suffice.
- Deployment and monitoring — we implement the pipeline and monitor quality in production, setting up automatic alerts for WER increases.
Timelines and Cost
The vocabulary approach (boosting + post-correction) takes 2–3 days. Whisper fine-tuning takes 2–4 weeks, including data collection and labeling. Adaptation cost is calculated individually—depending on corpus size, number of terms, and chosen methods. Savings on post-editing after adaptation reach 70%, with an ROI period of 3–6 months.
Additional Improvements
For critical domains (medicine, law), boosting is more effective when specifying the word form in each context. If sufficient data is available, Whisper fine-tuning yields a WER of 8–15% on medical data compared to 25% for the base model. For small audio volumes (less than 100 hours), we use boosting and post-correction—results close to fine-tuning in less time.
Guarantee: on all projects we set a target WER and confirm it on a test sample. Our team has 5+ years of experience and AWS and GCP certifications. Request a free diagnostic of your corpus—we will assess your current WER and choose the optimal solution. Contact us for a consultation.
Our pricing is transparent: boosting starts at $500, post-correction at $1,000, and the combined approach at $1,000–$5,000. For example, a typical medical client saves $12,000 annually after adaptation.