Tutorial 40: AI-Powered Answering Machine Detection — Whisper + ML Classifier Build a self-hosted answering machine detection (AMD) system that replaces Asterisk's built-in AMD() application with a Whisper-based speech recognition + machine learning classifier pipeline. Traditional AMD relies on energy detection and cadence analysis, achieving only 60-70% accuracy in real-world conditions — misclassifying live humans as machines (killing revenue-generating calls) and letting voicemail greetings through to agents (wasting expensive seat time). This tutorial's AI approach transcribes the first 3-5 seconds of answered audio using OpenAI's Whisper model, then feeds the transcript and audio features into a trained ML classifier that distinguishes human pickups from answering machines with 95%+ accuracy. The entire system runs on your own hardware with no per-call API costs, processes decisions in under 2 seconds, and continuously improves as you feed it new labeled data from your call center's actual traffic.…