Custom vocabulary support in speech-to-text: how to teach the model your terms

Custom vocabulary support in speech-to-text systems helps improve accuracy for domain-specific terms by adjusting the model's decoding behavior to recognize specific technical terms, brand names, or acronyms. This approach is particularly useful in contact centers, where generic models might misinterpret specialized terminology, leading to errors in CRM data, QA scorecards, and automated summaries. Runtime vocabulary lists are ideal for dynamic, frequently changing term sets, while model fine-tuning suits stable vocabularies in unique acoustic environments. It's crucial to manage vocabulary list length to avoid degrading accuracy by unnecessarily expanding the search space. Custom vocabulary should be updated in sync with product release cycles, and its impact measured using keyword error rate (KER) rather than global word error rate (WER) to ensure precise transcription of high-value keywords. Custom spelling complements this by performing string replacements for consistent misspellings. The integration of custom vocabulary requires careful configuration to optimize performance without adding significant latency or error rates, and it is often more cost-effective and efficient than model fine-tuning for dynamic environments.