what’s the best way to measure TTFS?

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yes, time to first speech

Thanks — great clarification. Below are concise, practical ways to measure Time To First Speech (TTFS) and what to instrument. Pick the approach that matches your SDK/environment and whether you want client-side, server-side, or end-to-end measurements. 1) Client-side (recommended for accuracy) • Start a timestamp when the participant calls join/connect. • Detect first speech using Voice Activity Detection (VAD) or the SDK’s audio track events: ◦ In browser JS: listen for the first audio level/VAD callback or the first outgoing audio packet/send event. ◦ On mobile/other SDKs: use the SDK’s VAD callbacks or when local microphone frames start being captured/sent. • TTFS = first-speech timestamp − join timestamp. • Optionally record participant ID, room ID, SDK version, network conditions for correlation. 2) Server-side / LiveKit-side (approximate) • Record when the participant’s track is Published or when the server first receives audio RTP packets for that participant. • TTFS_server = first-server-receive timestamp − server-join timestamp. • This measures when audio reaches LiveKit (useful for network/ingress delays) but may lag vs client-detected speech. 3) End-to-end perceived (listener-side) • On remote participants, record when the first inbound audio frame/playback starts or when their audio level/VAD indicates speech. • TTFS_e2e = first-playback timestamp − original-join timestamp (requires synchronizing clocks or propagating the join timestamp). 4) Reduce measurement noise / best practices • Use synchronized clocks (NTP) or send the client's join timestamp with events so receivers can compute deltas reliably. • Ignore very short spurious VAD hits by applying a minimum speech duration (e.g., 100–200 ms). • Capture network metadata (rtt, packet loss) alongside TTFS to diagnose causes. • For perceived user experience, prefer listener-side or end-to-end measurements; for debugging ingestion, prefer server-side. 5) Useful LiveKit features to leverage • Pre-connect audio buffer (Instant Connect) to reduce perceived TTFS by capturing mic audio before the room connection completes. • VAD/prewarmed models on clients to detect first speech sooner. If you tell me which SDK (browser JS, iOS, Android, or server) and whether you want client-side, server-side, or end-to-end metrics, I’ll give a short, concrete snippet or event list to implement measurement. Sources: Voice Agent Architecture: STT, LLM, and TTS Pipelines Explained | LiveKit | Build Your First AI Voice Agent in Python: Complete Tutorial | LiveKit | Understand and Improve Agent Latency | LiveKit | Understand and Improve Agent Latency | LiveKit | Agent speech and audio | LiveKit Documentation, with interactive elements