Turn Detection

Turn Detection identifies when a speaker has finished their conversational turn and it’s appropriate for an AI to respond. It solves a critical problem in voice AI: respond too early and you interrupt the speaker; wait too long and the conversation feels awkward.

Two Paths in Vision Agents

Turn detection works differently depending on your setup:

Custom pipeline:  Call audio → STT or TurnDetector → turn signals → LLM
Realtime mode:      Call audio ↔ Realtime API

1. Provider-built-in (STT output stream)

Many STT plugins emit TurnStarted and TurnEnded signals on their output stream. Turn detection happens inside the provider API — no separate plugin needed.

from vision_agents.core import Agent, User
from vision_agents.plugins import deepgram, gemini, getstream, inworld

agent = Agent(
    edge=getstream.Edge(),
    agent_user=User(name="Assistant", id="agent"),
    instructions="You are a helpful voice assistant.",
    llm=gemini.LLM(),
    stt=deepgram.STT(eager_turn_detection=True),  # built-in turns; eager = lower latency
    tts=inworld.TTS(),
)

2. External plugin

When your STT plugin has no built-in turn detection, add a TurnDetector plugin:

from vision_agents.plugins import smart_turn

agent = Agent(
    ...,
    stt=fast_whisper.STT(),
    turn_detection=smart_turn.TurnDetection(),
)

External plugins use Silero VAD plus neural models to predict turn completion — see Smart Turn and Vogent.

Turn Detection vs VAD

	VAD	Turn Detection
Question	”Is someone speaking?"	"Has the speaker finished?”
Output	Speech start/end timestamps	TurnStarted / TurnEnded turn signals
Intelligence	Simple audio analysis	Provider API or neural turn models
Best for	Detecting presence	Knowing when to respond

STT Plugins with Built-in Turn Detection

STT Plugin	Turn Detection
Deepgram	Always on; set eager_turn_detection=True for speculative early turns
ElevenLabs	Built-in via VAD commit strategy
Cartesia	Built-in; eager by default
AssemblyAI	Built-in
Sarvam	Built-in via VAD events

STT plugins without turn detection (Fast-Whisper, Wizper, Fish, Mistral, AWS) need an external plugin or fall back to using the final transcript as the end-of-turn signal.

External Turn Detection Plugins

Plugin	Description
Smart Turn	Combines Silero VAD, Whisper features, and neural turn completion models
Vogent	Neural turn detection with high accuracy prediction

For Realtime APIs (OpenAI, Gemini, AWS Bedrock, Qwen, xAI, Inworld), turn detection is built-in at the model level — no separate plugin needed. See Voice Agents — Realtime Mode.

When an STT plugin provides built-in turn detection (stt.turn_detection is True), the Agent automatically ignores any external TurnDetector plugin to prevent conflicts.

SDK Behavior

• TurnStarted — triggers barge-in interrupt in the transcribing flow
• TurnEnded(eager=True) — starts speculative LLM work for lower latency
• TurnEnded(eager=False) — confirms the turn is complete
• Pipeline signals (TurnStarted/TurnEnded on the STT output stream) are distinct from agent events (UserTurnStartedEvent, UserTurnEndedEvent)

Next Steps

Interruption Handling

Setup, tuning, and troubleshooting

Voice Agents

Wire turn detection into a pipeline

Smart Turn

Configure the Smart Turn plugin

Vogent

Alternative turn detection option