Most voice interfaces flatten speech into text and discard everything else; they were never designed to "listen" to human intent.
With 15-20% of the global population neurodivergent, this is an accessibility failure at infrastructure scale. This session presents a voice-first research prototype (not a diagnostic tool) that uses PyTorch-based Whisper to extract speech pattern markers: pacing, hesitation, repetition, and adapt responses based on those signals.
The speaker walks through containerising the Whisper inference pipeline, deploying it on Kubernetes with GPU acceleration, and designing adaptive systems that preserve privacy by keeping voice data local. Attendees will see a live demo comparing standard and ADHD speech patterns, learn how to build voice interfaces users can tailor to their own communication style, and leave with a forkable repo containing Dockerfiles, K8S manifests, and the full adaptive response pipeline.