Thanks — this is a great question and I understand the frustration: you want the agent to ignore brief backchannels like “okay” without pausing noticeably, while still stopping for meaningful single‑word interruptions like “what?” or “how?”. Quick acknowledgement of what I’m answering • I’m addressing both parts: (1) why you see the 2s pause even with

resume_false_interruption=True

, and (2) how to allow single‑word questions while ignoring single‑word backchannels. Why you see a brief pause • The agent uses

false_interruption_timeout

to wait and decide whether a short interruption is a false interruption (a backchannel) or the start of a user utterance. That waiting period (default 2s) is what causes the pause before the agent resumes speaking even when

resume_false_interruption=True

. Why

min_interruption_words=2

is problematic • Raising

min_interruption_words

to 2 prevents single‑word disruptions (so backchannels are ignored), but it also blocks valid single‑word questions (“what?”, “how?”). So

min_interruption_words

alone can’t reliably distinguish backchannels from real short questions. Recommended approach — combine settings + transcript-based filtering • Keep interruption thresholds permissive so legitimate one‑word interruptions are allowed, and use the transcript content to tell backchannels apart from real questions. Suggested configuration 1. Use: ◦

min_interruption_words=1

(allow single‑word interruptions) ◦

min_interruption_duration

set low (e.g., 0.2–0.4s) so short intentional interruptions register ◦ Reduce

false_interruption_timeout

(e.g., 1.0s or lower) to shorten the resume pause ◦

resume_false_interruption=True

Example (pseudo) config:

session = AgentSession(
    min_interruption_words=1,
    min_interruption_duration=0.3,
    false_interruption_timeout=1.0,
    resume_false_interruption=True,
    ...
)

Add transcript-based backchannel filtering (recommended) • Listen for finalized transcripts (

user_input_transcribed

with

is_final=true

). • Maintain a small set of common backchannels (e.g., "okay", "yeah", "uh-huh", "right", "mm-hmm", "sure"). • If the final transcript exactly matches (or is a short match against) a backchannel, treat it as a false interruption: do not generate a reply or reset the agent flow. • Otherwise, treat it as a real interruption and let the agent respond immediately. Example handler (conceptual):

BACKCHANNELS = {"okay", "yeah", "yes", "uh-huh", "mm-hmm", "right", "sure"}

@session.on("user_input_transcribed")
async def on_transcript(transcript):
    if not transcript.is_final:
        return
    text = transcript.transcript.strip().lower()
    if text in BACKCHANNELS:
        # ignore as backchannel
        return
    # treat as meaningful interruption (including single-word questions)
    session.generate_reply(user_input=text)

Why this solves both problems • The agent will resume quickly after brief false interruptions because

false_interruption_timeout

is shorter. • Single-word meaningful questions are still accepted because

min_interruption_words=1

. • Common backchannels are filtered by content instead of by word count, so they won’t trigger a full interruption/response. Optional refinements • Expand backchannel detection using simple heuristics: exact match, short length + common filler words, or a small ML classifier if you need higher accuracy. • If some backchannels are being transcribed with punctuation (e.g., “okay.”) or variants (“ok”, “k”), normalize and include those variants. • Tune

false_interruption_timeout

iteratively: shorter reduces pause but risks misclassifying two‑part utterances as false interruptions. If you’d like, I can: • Suggest a starter list of backchannel tokens to use for your language(s). • Share a small heuristic to detect single-word questions vs filler (e.g., presence of question words like “what/how/why/where/when” or a rising intonation flag if your STT provides prosody). • Help you pick exact values for

min_interruption_duration

and

false_interruption_timeout

based on how short the interruptions typically are in your use case. Which of the follow-ups above would you like to try first — a starter backchannel list, a heuristic for single-word questions, or tuned numeric values for time/word thresholds? Sources: Turns overview | LiveKit Documentation | Agent speech and audio | LiveKit Documentation | Agent session | LiveKit Documentation