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LLMs can judge speech recognition quality with near-human accuracy, blowing away traditional metrics like Word Error Rate.
Open-weight models can now generate realistic, long-form doctor-patient conversations with corresponding SOAP notes, providing a valuable resource for training and evaluating long-context audio reasoning systems.
Just 4 hours of speech data closes the modality gap in LLM-based ASR, rivaling full-dataset fine-tuning and unlocking effective domain adaptation.
LLM-based ASR can get a context boost without the compute cost: compress prior audio turns into learned latent tokens and retain transcripts to recover accuracy while shrinking the audio footprint.
