Custom Vocabulary Implementation for STT Systems
You integrate STT for a call center, but recognition of customer names and legal terms yields 40% errors. The model does not know "ОГРН", "СНИЛС", "ИНН". Every missed policy number or product code is lost data. Custom vocabulary is the fastest way to improve recognition of specific terms, names, and acronyms without retraining the model. It acts as a hint to the STT engine: "pay special attention to these words." Our certified engineers—over 8 years of experience and 30+ STT implementations—configure a vocabulary tailored to your domain in 2–4 hours. Word Error Rate reduction reaches 40% as early as the second day.
Why Custom Vocabulary Rather Than Model Retraining?
Retraining (fine-tuning) requires labeled audio data (at least 10 hours) and takes 2–4 weeks. Custom vocabulary delivers results in 1–2 days, reducing Word Error Rate by 30–50% for target terms. It does not change the architecture—you can modify the vocabulary on the fly without service downtime. For most business scenarios (order processing, legal consultations), this is sufficient.
Implementation for Major Providers
AWS Transcribe Custom Vocabulary
import boto3 transcribe = boto3.client('transcribe') # Create vocabulary from file (S3) transcribe.create_vocabulary( VocabularyName='corporate-terms-v1', LanguageCode='ru-RU', VocabularyFileUri='s3://my-bucket/vocabulary.txt' ) # Format of vocabulary.txt: # Phrase\tSoundsLike\tIPA\tDisplayAs # Б-Ф-И-О\tbeh ef ee oh\t\tБФИО # ИНН\tin en en\t\tИНН Azure Custom Speech
# Add domain adaptation data via Azure Portal or REST API # Supports: pronunciation dictionary, phrase list import requests phrase_list = { "kind": "PhraseList", "locale": "ru-RU", "phrases": ["ОГРН", "СНИЛС", "КПП", "расчётный счёт"] } Faster-whisper with Initial Prompt
model = WhisperModel("large-v3", device="cuda") # Initial prompt helps the model focus on relevant vocabulary initial_prompt = "ИНН, ОГРН, СНИЛС, КПП, расчётный счёт, генеральный директор." segments, _ = model.transcribe( audio, initial_prompt=initial_prompt, language="ru" ) The initial_prompt method works unreliably for long files—the prompt is processed only for the first window. For production, we recommend using the provider's built-in custom vocab.
Approach Comparison
| Method | Time to Implement | WER Reduction | Latency Overhead | Maintenance Complexity |
|---|---|---|---|---|
| AWS Custom Vocabulary | 1–2 days | 30–50% | 5–10% | Low |
| Azure Phrase List | 1–2 days | 20–40% | 5–10% | Low |
| faster-whisper initial prompt | 1 hour | 10–20% | 0% | Medium (requires testing) |
| Model fine-tuning | 2–4 weeks | 50–70% | 0% | High |
Custom vocabulary works 10x faster than model retraining and provides sufficient accuracy for 90% of tasks.
How We Reduce WER by 40% in 2 Days
The process includes auditing the current STT, designing a domain vocabulary, implementing via the chosen provider's API, and A/B testing on 100+ audio files. We use sound similarity (SoundsLike) for acronyms and pronunciation variants (IPA) for complex words. The result is measurable accuracy gain without infrastructure changes. For one insurance project, WER on terms "ДМС", "ВЗР", "ОМС" dropped from 55% to 12%.
What If the Vocabulary Doesn't Help?
Sometimes the custom vocabulary yields less than 10% improvement—a signal of deeper issues: poor audio quality, model not adapted to noise, or context overwhelmed by homonymy. In such cases, we recommend combining vocabulary with lightweight fine-tuning or data augmentation. We perform diagnostics and propose the optimal strategy.
Common Configuration Mistakes
- Not specifying pronunciation variants for acronyms (e.g., "БФИО" recognized as "behfeeoh").
- Overly long phrases (more than 10 words)—degrade performance.
- Ignoring regional dialects—pronunciation may differ for accented Russian.
- Lack of a test dataset—hard to tell if WER improved.
Setup Process
| Stage | Duration | Result |
|---|---|---|
| Analysis | 0.5 day | List of 50–100 target terms |
| Design | 0.5 day | SoundsLike and IPA format |
| Integration | 0.5 day | Vocabulary connected to STT |
| Testing | 0.5 day | WER on a representative sample |
| Deployment | 0.5 day | Running in staging and production |
Keeping the Vocabulary Up to Date
- Versioning: every change is a new Git tag (v1.0, v1.1).
- Automatic updates: CI/CD ingests new terms from Jira/spreadsheet.
- Monitoring: alerts when accuracy drops by more than 5%.
A typical mistake is not accounting for homonyms. For example, "БФИО" may be recognized as "behfeeoh". In AWS Transcribe, the SoundsLike column is used for this.
Timeline: basic integration takes 1–2 days, including vocabulary population. We can evaluate your project within 2 days. Get a consultation on your case within 1 day—our engineers hold AWS AI and Azure AI Engineer certifications.
For detailed study, refer to the official documentation: AWS Transcribe Custom Vocabulary and Wikipedia: Speech recognition.