Keyword boosting: teaching Speech-To-Text your vocabulary

Speech-to-Text models excel at transcribing everyday speech but often struggle with rare, specific terms, such as newly introduced products or unique names, as they tend to default to more common, similar-sounding words. Keyword boosting addresses this issue by allowing users to provide a short dictionary of important terms, enhancing the model's accuracy by increasing the likelihood of correctly transcribing these words in real-time without retraining. This technique proves particularly useful in domains with specialized vocabulary, such as sports commentary or medical terms, where the model's baseline performance might falter. The boost parameter, adjustable in the model setup, determines the strength of preference for the listed keywords, with a typical recommended value of 3, which effectively recovers rare vocabulary while minimizing side effects. Users can implement keyword boosting via a simple JSON configuration in the model's WebSocket setup, making it a valuable tool for improving transcription accuracy in scenarios where specific terms are crucial.

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