Earlybird Predicts Speech Before Words End

Earlybird makes negative-latency speech recognition practical, but its biggest value is giving agents an early, confidence-weighted signal rather than a transcript they must blindly trust.

• –Runs about twice real time on one CPU thread, making local deployment unusually accessible
• –Predicts 69% of audiobook words correctly before they end, substantially ahead of conventional streaming models
• –Confidence scores let developers decide when to prefetch, classify intent, or start planning
• –Early predictions can rewrite frequently, so production agents need provisional-text handling and commit policies
• –Accuracy falls sharply in background noise, and the model currently supports English only