How Can I detect a Bot ?

Thanks for reaching out, Saurabh. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

In a Voice AI call, sometimes user is talking to themselves or to a bot

Thanks — that helps. I understand you want to detect when a participant in a Voice AI call is a bot (or when a user is effectively talking to themselves/an automated voice). I can recommend practical checks and approaches, but first a couple quick clarifying questions so I tailor suggestions to what you can actually observe and control: • Where does the Voice AI run and how is it connected to the call? ◦ Is the bot a separate endpoint that joins the same LiveKit room as a participant, or is the AI audio injected server-side (e.g., mixing audio tracks before sending to the room)? • What signals can you access during the call? ◦ Examples: participant metadata, authentication identity (user IDs / tokens), participant join/leave timestamps, WebRTC stats (

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mute/unmute, jitter, packet loss), client IP addresses, user agent, or audio-level metrics (voice activity detection / VAD, energy, pitch). • Do you control the authentication flow for all participants, or can guests join anonymously? • Do you want to only detect (flag) likely bots, or to also automatically block/kick them? • Is the bot synthetic-sounding (TTS) or does it emulate human prosody? Do you have transcripts / ASR outputs available in real time? While you answer, here are practical detection strategies you can consider depending on what you can access: • Identity & metadata checks (reliable, low cost) ◦ Require authenticated identities and record which user IDs correspond to known AI agents. Flag any participant whose ID or metadata indicates an AI. ◦ Maintain a whitelist/blacklist of known bot accounts. • Session & behavioral signals (good for heuristics) ◦ Join pattern: bots often join at predictable times or immediately after certain triggers. Track timing and frequency of joins. ◦ Turn-taking regularity: bots may have highly regular response latencies (very consistent RTTs). Compute per-participant response latency distribution; low variance + short fixed delay can indicate automation. ◦ Simultaneous audio: detect if one participant’s audio is always speaking immediately after another with deterministic timing. • Audio & WebRTC signals (requires audio or client stats) ◦ Voice Activity Detection (VAD): check if audio segments have unnatural on/off patterns (e.g., perfectly clipped starts/ends). ◦ Spectral or prosody features: TTS often has narrower pitch variation, more uniform energy envelopes, or missing microphone noise. Run lightweight feature checks (pitch variance, spectral entropy). ◦ Playback-loop detection: if the same audio frames are being replayed, detect repeated identical fingerprints (hash short audio frames). ◦ WebRTC stats: many bots use server-side injection or synthetic senders that produce nearly zero packet loss and perfect timing. Compare per-participant RTCP/WebRTC stats for unusual consistency. • ASR / NLP-based checks (stronger, compute cost) ◦ Compare conversational semantics: if a participant produces only short canned replies or rephrases input text, that could be a bot signature. ◦ Use a classifier on ASR transcripts to detect patterns typical of bots (repetitive phrasing, templated replies, limited vocabulary). ◦ Ask challenge questions requiring contextual reasoning or non-trivial follow-ups; measure response correctness/latency. • Network & client fingerprints ◦ IP or region mismatch: check if the participant’s IP/location or user-agent is an outlier compared to expected users. ◦ Browser/SDK fingerprint: server-side bots may use consistent SDK versions or server headers. • Active probing (if acceptable) ◦ Send a short randomized audio token or challenge and expect it to be repeated or acknowledged correctly. Bots that do not understand or mirror will fail. Next steps I can help with (choose one or more): • Provide a prioritized checklist tailored to the signals you have (auth, WebRTC stats, audio, transcripts). • Draft code examples or pseudo-code for computing response-latency distributions, simple VAD-based checks, or audio fingerprinting. • Design a lightweight classifier using a few acoustic features and response-timing features to flag likely bots. • Recommend which checks to run in real time versus offline scoring. Tell me which signals you can access and whether you prefer flagging vs blocking; I’ll give a concrete implementation plan and sample checks you can start with.