can i provide STT in the VAD to help detect voice activity by STT ?

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

I want to ignore people taking in the background, how can i do that?

Thanks — you want to ignore people talking in the background. Do you mean: • ignore background talk for Voice Activity Detection (so VAD doesn't trigger), or • ignore background talk in transcriptions (so STT output skips background speakers), or • ignore background talk in the app UI (so remote participants who are in background aren't shown as speaking)? A little about options you can use while I wait for which you mean: • For VAD: tune VAD parameters (increase

min_silence_duration

, raise thresholds) so brief/background speech doesn't register as active speech. VAD only uses audio features, so adjust sensitivity rather than feeding transcripts. • For STT/transcription: use an STT provider that supports speaker diarization or semantic endpointing, or post-process transcripts to filter low-confidence segments or non-primary speakers. • For the app/UI: detect the dominant speaker and mute/ignore others programmatically (e.g., lower volume, hide speaking indicators) using per-participant audio levels or the turn-detection output. Which of the three do you want to focus on? Share your platform (browser or native), whether you control the client code, and an example of the background case (very quiet background talk vs. loud nearby conversation). I’ll give specific settings and steps.

is there any way i can tell VAD the min frequency where it should trigger?

Short answer: No — you cannot set a minimum frequency for VAD to trigger. VAD in LiveKit (Silero VAD) works from raw audio and doesn't expose frequency-domain controls. You can only tune sensitivity via parameters like

activation_threshold

,

min_speech_duration

,

min_silence_duration

, and

sample_rate

. If you need frequency-based filtering, two alternatives: • Preprocess audio on the client/server with a band-pass or high-pass filter to remove unwanted frequencies before VAD sees the audio. • Keep VAD for low-level speech detection and add a post-VAD step that inspects the audio spectrum (or uses an ML model) to ignore segments lacking the target frequency content. Which approach would you prefer help with — adjusting VAD params, adding an audio filter, or implementing post-VAD spectral filtering? Sources: Silero VAD plugin | LiveKit docs

what dose activation_threshold do?

You're asking what

activation_threshold

does for VAD — good question. • What it is:

activation_threshold

sets how strongly the VAD model must believe a frame contains speech before it marks that frame as speech. It's a confidence/sensitivity cutoff (default ~0.5). • Practical effect: ◦ Higher values → more conservative VAD (fewer false positives; brief/quiet sounds less likely to trigger). ◦ Lower values → more sensitive VAD (detects softer/shorter speech but may produce more false positives from noise). • Tuning tips: ◦ If background chatter triggers too often, raise the threshold. ◦ If quiet speakers are missed, lower the threshold. ◦ Test with representative audio and adjust incrementally. If you want, tell me your typical sample rate and an example scenario (quiet background vs. loud TV) and I’ll suggest a starting value to try.

currently people chatting in backgroud or saying something to primary speakr is also pausing the agent thos nothing comes up in the transcription as the audio is too low

Thanks — I hear you: background chatter is causing VAD to pause the primary speaker even though that background audio is too low to transcribe. Here are concrete steps to reduce those false pauses: 1. Tune VAD sensitivity • Increase

activation_threshold

to make VAD more conservative (start +0.1 from your current value; keep testing). • Increase

min_speech_duration

so very short bursts don’t count as speech. • Increase

min_silence_duration

so the agent doesn’t treat brief interruptions as end-of-turn. 1. Preprocess audio before VAD • Apply a high-pass/low-pass or band-pass filter to remove frequency bands typical of background noise. This prevents irrelevant energy from reaching VAD. • Apply noise suppression / gain control so the primary speaker’s signal dominates. 1. Post-process VAD decisions • Ignore VAD activity from participants with consistently low RMS/level for that segment (treat them as background). • Require both VAD+level threshold for a participant to trigger an agent pause (e.g., VAD=true AND avg_level > X dB). 1. Combine STT for turn detection • Keep VAD for low-level detection but use STT end-of-speech signals (or STT confidence) to confirm an actual utterance before pausing the agent. Which of these would you like step-by-step help with? • I can suggest specific parameter values if you tell me your current

activation_threshold

,

min_silence_duration

,

sample_rate

, and an example (quiet background vs. loud nearby conversation). • Or I can provide code snippets for client-side filtering/noise suppression for your platform (browser or native).

loud nearby conversation

Got it — loud nearby conversation is triggering VAD and pausing the agent. Here are focused, practical steps you can try now. Tuning VAD (start here) • Increase

activation_threshold

(make VAD more conservative). Try +0.1 to +0.2 from your current value and test. • Increase

min_speech_duration

so very short bursts don't count as speech. • Increase

min_silence_duration

so brief interruptions don’t end the primary speaker’s turn. Audio preprocessing (prevent VAD from seeing the background) • Apply noise suppression / automatic gain control so the primary speaker’s voice dominates. • Apply a band-pass or high-pass filter to remove frequency bands common to background chatter before VAD processes audio. • If you can, perform directional/source separation on the client (beamforming or voice activity per mic) so VAD sees the primary mic/speaker only. Post-VAD logic (add robustness) • Require VAD + a per-participant level threshold (e.g., VAD=true AND avg_level > X dB) before pausing the agent. • Ignore very short VAD activations from non-dominant speakers by checking RMS or recent speaking history. • Use STT end-of-speech or confidence together with VAD to confirm real utterances before actioning a pause. Recommended immediate test 1. Raise

activation_threshold

by +0.1 and increase

min_speech_duration

and

min_silence_duration

modestly. 2. If that’s insufficient, enable noise suppression and a simple high-pass filter on the client, then re-test. If you want, tell me: • your current VAD settings (

activation_threshold

,

min_silence_duration

,

min_speech_duration

,

sample_rate

), and • your platform (browser or native) — and I’ll suggest concrete parameter values and, if needed, a short code snippet for filtering or level-based gating.