The ASR Industry Is Solving the Wrong Problem
Speech recognition vendors have spent decades perfecting noise filtering. Research shows it often hurts accuracy. A different approach to acoustic intelligence is needed....
The Speech-to-Speech Revolution: When 300ms Changes Everything
Speech-to-speech AI has crossed the 300ms latency threshold where interactions feel like genuine conversation. What this means for voice interfaces and where it still fails....
Why Every ASR System Lies About Its Accuracy
ASR accuracy claims are based on ideal conditions. Real-world performance with background noise, accents, and domain jargon drops 30-50%. What AMBIE taught us about honest metrics....
Domain-Specific ASR: Why General Models Fail in the Real World
Legal terminology, manufacturing jargon, call center scripts - each requires specialized training. The myth of 'one model to rule them all.'...
The ASR Privacy Paradox
ASR systems need training data. Training data contains sensitive audio. How federated learning solves the conflict between ML requirements and privacy laws....
Speaker Diarization: The Hardest Problem Nobody Talks About
Why Zoom transcripts attribute quotes to the wrong people. The cocktail party problem isn't solved - it's hidden. Multi-device synchronization as a workaround....
Voice to Context to Action: A Framework for Operational Voice AI
Radio traffic is operational intelligence that vanishes into air. This framework for voice AI captures, interprets, and acts on voice data in minutes instead of hours....
Why Voice AI Demos Always Work (And Production Never Does)
Voice AI demos work perfectly. Production deployments fail. After a decade building speech systems, here's why the gap exists and how to bridge it....