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Turn Detection automatically identifies when a speaker has completed their conversational turn and it’s appropriate for an AI to respond. It solves a critical problem in voice AI: knowing when to respond. Respond too early and you interrupt the speaker. Wait too long and the conversation feels awkward. Turn detection analyzes speech patterns and context to find that perfect moment.

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How does turn detection work?

Here’s what happens when audio flows through a turn detection system:
  1. Audio Input: Raw audio data enters the turn detection system for processing.
  2. AI Turn Detection Model: The audio is processed through an AI model that analyzes conversational patterns and speech characteristics.
  3. AI Turn Detection Engine: This engine performs three key functions:
    • Content Analysis: Examines what the speaker is saying to identify complete thoughts or sentences
    • Speech Pattern Analysis: Analyzes voice characteristics like pitch, tone and rhythm that signal someone is finishing
    • Context Awareness: Determines whether a pause means “I’m done talking” or “I’m still thinking”
  4. Turn Completion Signal: The output indicates whether the speaker has finished (time to respond) or is still speaking (keep listening).

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How does it work with Vision Agents?

With Vision Agents you get a plugin system that handles all the detection logic for you. Here’s how it works in your calls:
  1. Call Audio: Audio from your video call enters the system.
  2. Turn Detection Plugins: The plugins buffer and process incoming audio through the AI analysis pipeline.
  3. AI Analysis: The system analyzes speech patterns and conversation context to predict turn completion. It emits turn_started events when speakers begin and turn_ended events when they finish.
  4. Your Application: Your application receives turn events to manage conversation flow. You know exactly when to respond, when to keep listening, or when to yield control back to the user.

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Worked example

Let’s walk through a real-world scenario to see how turn detection transforms your application’s conversational intelligence. Imagine you’re building an AI voice assistant for customer service. Here’s how turn detection makes the interaction feel natural: The Scenario: A customer calls to inquire about their order status and has a complex question. What Happens:
  1. The customer says: “Hi, I’m calling about my order…” and pauses to check their order number.
  2. Without turn detection, the AI might interrupt here. But turn detection recognizes the incomplete sentence and continues listening.
  3. The customer continues: “…it’s order number 12345, and I wanted to know when it will arrive.” The system detects the complete thought and turn-ending cues.
  4. Turn detection emits a turn_ended event with high confidence, signaling it’s time to respond.
  5. Your AI responds promptly, creating a smooth conversational experience without interruptions or awkward delays.

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Turn Detection vs Voice Activity Detection

Voice Activity Detection (VAD) and turn detection solve different problems:
  • VAD answers: “Is someone speaking right now?”
  • Turn Detection answers: “Has the speaker finished their turn?”
VAD detects when speech starts and stops. Turn detection determines when it’s appropriate to respond. Using VAD alone often causes interruptions during natural pauses or awkward delays after speech ends. Turn detection adds conversational intelligence on top of VAD.

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Applications of Turn Detection

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Voice Assistants and AI Agents

Build AI that responds at the right moment, neither interrupting users mid-sentence nor waiting awkwardly after they finish. Create customer service bots, virtual receptionists and voice assistants that feel conversational rather than robotic.

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Real-time Translation

Capture complete thoughts before translating, avoiding confusion from incomplete sentences. Determine optimal moments to begin translation so listeners receive coherent, complete messages.

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Meeting Intelligence

Identify natural break points for automated summarization, action item extraction and highlight generation. Understand when speakers have completed their contributions to create accurate meeting transcripts.

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Interview and Coaching Tools

Build AI interviewers or coaches that engage in realistic dialogue, responding appropriately without interrupting the user’s train of thought. Enable natural back-and-forth conversation.

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Accessibility Features

Create responsive voice control systems that fully capture commands before processing while maintaining quick response times. Improve reliability for users who depend on voice interfaces.

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Conversation Analytics

Analyze conversation dynamics and interaction quality by understanding natural conversational flow. Measure metrics like turn-taking patterns, response timing and dialogue balance in customer service or team meetings.
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