Modern speech recognition systems are built on end-to-end (E2E) neural network architectures that map raw audio directly to text - without separate acoustic models, pronunciation dictionaries, or language models. While this simplifies the pipeline and delivers strong general accuracy, E2E models can struggle with domain-specific terminology, rare words, or application-specific phrases that are underrepresented in training data.
Context biasing addresses this gap.
What is Context Biasing?
Context biasing - also known as on-the-fly adaptation or shallow fusion - is a technique that incorporates external contextual information into the speech recognition process without retraining the model. During decoding, the model integrates additional context such as a list of keywords, phrases, or a supplementary language model. This adjusts the probability distribution over possible outputs, effectively biasing the recognizer toward terms relevant to the specific application or domain.
The result is a model that behaves like a general-purpose recognizer for most speech, but prioritizes the vocabulary that matters most for your use case.
Benefits
Improved accuracy
- Word Error Rate reduction - significant decrease in WER for context-specific or domain terms
- Rare word recognition - enhanced ability to recognize low-frequency or novel words that the base model would otherwise miss
Enhanced user experience
- Personalization - tailors recognition to individual users, products, or workflows
- Relevance - delivers more accurate and meaningful transcriptions in specialized contexts
Customization and flexibility
- On-the-fly updates - new context can be added at runtime without retraining the model
- Domain adaptability - easily applied across different industries and use cases
How It Works
During the decoding phase, the SR engine integrates an external context list alongside its standard language model. Each term in the list is assigned a weight that influences how strongly the decoder is biased toward that term. The higher the weight, the more the recognizer favors that word or phrase when the audio is ambiguous.
This means the base model is never modified - context biasing is applied at inference time, making it fast to update and easy to maintain.
Implementation at SESTEK
SESTEK SR uses static context biasing, where predefined lists of context-specific words are applied during recognition. These lists are curated to include terms relevant to specific deployments - industry jargon, product names, client-specific vocabulary, or commonly used phrases.
How it is integrated:
- Static context lists - integrated directly into the SR pipeline, ensuring bias is applied consistently across all recognition requests
- Weighted biasing - predefined weights are assigned to specific terms, allowing fine-grained control over how strongly each word is prioritized
Use Cases
In-car voice assistants
Context biasing allows automotive voice systems to accurately recognize vehicle-specific commands such as "turn on the AC" or "open the sunroof" - terms that are unlikely to appear in general-purpose training data.
Medical transcription
In clinical environments, transcription systems can prioritize medical terminology, allowing doctors to dictate complex terms like "angioplasty" or "myocardial infarction" with confidence.
Customer service virtual assistants
Virtual assistants can recognize product names, customer IDs, and service-specific terms - even when they are uncommon in everyday speech - improving accuracy and customer satisfaction.
IVR systems
In call centers, context biasing ensures that IVR systems accurately recognize product-specific terms, common customer queries, and action phrases such as "check balance" or "account details."