Speech recognition systems handle rare or technical terms through a combination of specialized language models, custom vocabulary lists, and context-aware processing. These systems rely on statistical patterns and predefined data to convert audio into text, so uncommon words not frequently present in training data pose challenges. To address this, developers often enhance the system’s vocabulary and adjust its probability calculations to prioritize domain-specific terms when needed. For example, a medical app might need to recognize terms like “hemochromatosis” or “neutropenia,” which standard models might miss.
One common approach is using custom pronunciation dictionaries or phonetic annotations. Speech recognizers map sounds to text using grapheme-to-phoneme models, but technical terms often have non-intuitive pronunciations. By explicitly defining how a word like “EGFR” (a gene abbreviation) is pronounced (“ee-jee-eff-ar”), developers reduce errors. Some systems also allow dynamic vocabulary injection, where context-specific terms are temporarily added to the active word list. For instance, a coding assistant might load terms like “gRPC” or “Kubernetes” only when detecting programming-related speech, improving accuracy without bloating the general model.
Additionally, modern systems use context to disambiguate tricky terms. If a user says “administer 5mg of L-DOPA,” the recognizer leverages surrounding words (“administer,” “mg”) to infer that “L-DOPA” refers to the medication instead of a random acronym. Some frameworks like Kaldi or Whisper support fine-tuning on domain-specific audio datasets, allowing models to learn both the acoustic patterns and semantic context of technical jargon. For rare terms with limited training data, hybrid approaches combining rule-based pattern matching (like chemical nomenclature rules for terms like “dichlorodifluoromethane”) with neural networks often provide the best balance of flexibility and precision.