Interruption Engine
Configure VAD, Turn-Taking, and Barge-in logic for natural conversations.
The Interruption Engine is the referee of the conversation. It manages the delicate balance between "Listening" and "Speaking."
Visualizing the Logic
Before configuring parameters, it helps to understand how the agent makes decisions in real-time when it hears a sound.
1. Turn-End Detection
"Are you done talking?"
How does the agent know the user has finished their sentence and is waiting for a response?
VAD (Voice Activity)
Fastest. Detects silence. If the user stops making sound for X milliseconds (e.g., 500ms), the turn ends.
Transcription
Balanced. Uses the STT provider to check for a grammatically complete sentence (Final Segment).
LLM Decision
Context Aware. Sends the transcript to an LLM. It understands that a user saying "I want..." is not done, even if they pause for 2 seconds.
ML (Smart Turn)
Experimental / High Fidelity. Uses a specialized ML model to predict turn-end based on tone and prosody.
Deep Dive: ML Smart Turn (Pipecat)
This strategy uses the open-source Smart Turn model by Pipecat AI. It analyzes the raw audio stream to detect when a user has finished speaking based on intonation, not just silence.
Current Limitations
While this is the "Gold Standard" for natural conversation, it is currently Experimental.
- Numbers & Emails: It may struggle to detect turn-end correctly when a user is reciting long number sequences or spelling emails.
- Language Support: Check the GitHub Repo for the latest supported languages.
- Recommendation: Use standard VAD for IVR/Data Collection bots. Use Smart Turn for conversational/support bots.
2. Interruption Handling (Barge-In)
"Did you just interrupt me?"
When the agent is speaking, and the user makes a sound, the agent must decide whether to stop or ignore it.
Pause Triggers
How much "noise" triggers the agent to pause?
- Pause via VAD: Triggered by continuous sound duration (e.g., user speaks for 400ms). Best for low latency.
- Pause via Word Count: Triggered only after X words are transcribed. Prevents pausing for coughs or door slams.
AI Verification (False Positive Protection)
This feature prevents the agent from stopping due to "Backchanneling" (the user saying "Uh-huh", "Yes", or "Right" to show they are listening).
How it works:
- User speaks > Agent Pauses.
- The audio is transcribed and sent to a fast LLM.
- The Check: The LLM compares the user's input against the Agent's current speech.
- Input: "Wait, stop." > Agent Stops and handles the new query.
- Input: "I see..." > Agent Resumes speaking exactly where it left off.
Context Recovery
If the interruption was valid (Agent Stops), you can enable "Include Interrupted Speech in Next Turn". This ensures that whatever the agent didn't get to say is fed back into the LLM context, so the logic isn't lost.
3. Turn-by-Turn Mode
Disable Interruptions
For specific use cases (like taking a dictation, a formal interview, or legacy radio-style comms), you might want to disable interruptions entirely.
- Behavior: The agent will ignore all user audio until it has finished playing its current TTS response.
- Use Case: Highly disciplined workflows or environments with extreme background noise (Construction sites, Busy streets).
Future Vision: Dynamic Context Adaptation
Currently, Interruption and Turn-End settings are Static—they apply globally to the Agent for the entire duration of the call.
However, we understand that conversation flow changes based on context.
- Scenario A (Yes/No): "Do you want to proceed?" > Requires short silence threshold (e.g., 400ms) for a snappy feel.
- Scenario B (Data Entry): "Please spell your email address." > Requires long silence threshold (e.g., 1500ms) because users pause while spelling.
We are actively developing Context-Aware Turn Taking. This will allow the Agent (or specific Script Nodes) to programmatically adjust the VAD sensitivity, silence duration, and interruption strategy mid-call based on the specific question being asked.