Thanks — this is clear and helpful context. I’ll address both questions: (A) which turn-detection model and settings are fastest for English, and (B) how to improve your current VAD-only setup for better UX with non-native speakers. Acknowledgement • You removed turn detection to reduce EOU latency from ~2–3s to ~0.5s, using Silero VAD with the settings you shared. • You want low latency but fewer false cut-offs for non-native speakers. A) Fastest turn-detection option for English (recommended) • Use the English-optimized turn detector: EnglishModel() (or MultilingualModel if you prefer one model for many languages). Both run on CPU and are designed for real-time inference; EnglishModel() is the fastest for English. • Combine the turn detector + VAD + tuned endpoint delays to keep latency low while avoiding mid-sentence cut-offs. Recommended starting config (fast + robust):

from livekit.plugins.turn_detector import EnglishModel
from livekit.plugins import silero

vad = silero.VAD.load(
  min_silence_duration=0.30,      # avoids very-short pause cut-offs
  activation_threshold=0.65,
  deactivation_threshold=0.20,
  sample_rate=16000               # prefer 16kHz for better accuracy if your pipeline supports it
)

session = AgentSession(
  turn_detection=EnglishModel(),
  vad=vad,
  stt=deepgram.STT(model="nova-3", language="en"),
  min_endpointing_delay=0.3,      # shorter wait before deciding EOU
  max_endpointing_delay=2.0,
  preemptive_generation=True,
)

Why this helps: • The turn detector uses semantic cues so it won’t cut off natural pauses the way VAD-only systems do. •

min_endpointing_delay=0.3

lets you approach ~500ms EOU while still allowing the model to confirm end-of-turn. B) If you keep your current (no turn detection) setup — improvements for non-native speakers • Increase

min_silence_duration

from 0.15 to ~0.3–0.4s. Non-native speakers often pause longer; 0.15s is too aggressive and causes premature agent starts. • Lower activation threshold slightly (e.g., 0.55–0.60) so softer voices are detected reliably. • Keep

deactivation_threshold

a bit lower (0.15–0.20) to avoid stopping while a speaker trails off. • Prefer 16 kHz audio if feasible — better fidelity improves VAD/STT performance. • Keep

preemptive_generation=True

— it helps responsiveness while waiting for the full endpoint decision. • Add a short pre-speech pause before agent playback (e.g., 150–250ms) to avoid interrupting lingering speech and to feel more natural. • Consider STT-based endpointing as a middle ground: use the STT provider’s endpointing (turn_detection="stt") for faster, content-aware decisions if full turn detector is too heavyweight. Example adjusted VAD-only config:

vad = silero.VAD.load(
  min_silence_duration=0.35,
  activation_threshold=0.58,
  deactivation_threshold=0.18,
  sample_rate=16000
)

Quick checklist to iterate: • Try EnglishModel() with min_endpointing_delay=0.3 and 16kHz audio. Measure EOU and cut-offs. • If CPU or latency constraints prevent turn detection, use the adjusted VAD params above and STT-based endpointing. • Test with non-native speakers and increase min_silence_duration until false cut-offs drop to acceptable levels. If you want, tell me: • whether you can switch to 16 kHz audio, • whether CPU is constrained (so I can suggest lighter-weight settings), • and a short sample of the problematic scenarios (long pauses, soft speech, filler words). I’ll suggest tuned values or a short A/B test plan. Sources: Turns overview | LiveKit docs | LiveKit turn detector plugin | LiveKit docs | Agents Framework | livekit/livekit_composite | DeepWiki | Silero VAD plugin | LiveKit docs | Improving voice AI's turn detection with transformers