AI-Powered Call Detector Aims to Identify Live Humans in Under 2 Seconds

A developer has outlined a project to create a "Live Human Detector" for outbound phone calls, aiming to automatically identify when a call has been answered by a real person as opposed to being stuck in a queue or hitting voicemail. The system would listen to the audio stream post-IVR navigation and classify the call phase within a 1–2 second window with high confidence. This is not a typical AMD (Answering Machine Detection) tool; it must differentiate between prerecorded voice announcements (RVA), text-to-speech (TTS), ringback tones, voicemail beeps, and actual human speech.

Challenges include distinguishing professionally recorded announcements from in-house ones, detecting the subtle click and silence when a call is answered, and handling voicemail messages that sound similar to announcements. The approach involves training a machine learning audio classifier using labeled data, analyzing waveforms or spectrograms via Fast Fourier Transform. The developer plans to rely on Claude Code for implementation and has referenced several academic papers and open-source resources, including the YOHO paper from MDPI and Hugging Face's audio classification pipeline. Initially, no speech-to-text will be used, but it may be added later to improve confidence in ambiguous labels such as RVA, TTS, or voicemail. The project addresses a significant pain point for anyone making automated outbound calls—from customer support to sales—by reducing time wasted in queues.