### Why is this change needed?
Automatic speech recognition (ASR) systems are trained on massive
datasets of general speech, which means they excel at common vocabulary
but struggle with domain-specific terminology. In business
contexts—earnings calls, medical dictation, legal proceedings—the most
critical words are often the ones the model has rarely or never seen:
company names like "Saoirse Ronan," product names like "Newrez," or
technical jargon unique to an industry. Without intervention, these
high-value terms get transcribed as phonetically similar but incorrect
common words, turning "Nequi" into "NECI" or "Bose" into "Boz."
Keyword boosting (also called context biasing or vocabulary rescoring)
addresses this gap by incorporating domain knowledge at inference time.
The system is given a list of expected vocabulary terms and uses
acoustic evidence—typically CTC log-probabilities—to determine whether
the audio actually supports replacing a transcribed word with a
vocabulary term. This isn't blind substitution; a well-designed rescorer
computes scores for both the original transcription and the candidate
vocabulary term, only making replacements when the acoustic evidence
favors the domain term. The "context-biasing weight" parameter allows
tuning how aggressively to prefer vocabulary terms.
The real-world impact is substantial. In our earnings call benchmark,
vocabulary rescoring improved F-score from baseline to 92.2%, correctly
identifying 1,094 out of 1,271 domain-specific terms. Multi-word alias
support.
This PR builds on the work from
https://github.com/FluidInference/FluidAudio/pull/240
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Co-authored-by: Alex-Wengg <hanweng9@gmail.com>
Sachin DesaiandAlex-Wengg
Alex
Brandon Weng
Brandon Weng