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when setting the turn_detection = "stt" and using ...
# ask-ai
c
when setting the turn_detection = "stt" and using deepgram as a stt provider is it solely relying on deepgram's mechanisms to interrupt an agent midspeech and detect user speech?
t
Thanks for reaching out, Clovis. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
When setting 
turn_detection = "stt"
and using Deepgram as the STT provider, the system does not solely rely on Deepgram's mechanisms for interrupting an agent midspeech or detecting user speech. Instead, it uses a hybrid approach that combines Deepgram's speech boundary detection capabilities with LiveKit's own turn detection logic. Here’s how it works: • Deepgram's Role: Deepgram provides endpointing signals, such as the 
speech_final
events and an 
endpointing_ms
parameter, which indicate when speech has likely ended based on silence duration and speech patterns. • LiveKit's Additional Processing: LiveKit applies its own endpointing logic on top of the STT signals. For example, it uses configurable delays (
min_endpointing_delay
and 
max_endpointing_delay
) to determine when to finalize a turn based on the confidence level of the detection. • Interruption Handling: For interrupting an agent midspeech, STT turn detection is not the primary mechanism. Instead, Voice Activity Detection (VAD) is recommended for more responsive interruption handling. This is particularly useful when using both LLM and STT. In summary, the system leverages both Deepgram's capabilities and LiveKit's processing to ensure robust turn detection and interruption handling. Sources: Speech & Audio Processing | livekit/livekit_composite | DeepWiki | Turn detection and interruptions | LiveKit Docs | livekit-plugins/livekit-plugins-turn-detector/README.md | plugins/deepgram/src/stt.ts
c
but deepgram mechanisms are good to handle barge-in, so is there a way to rely only on this and not the livekit potential vad models or settings?
t
Hey there! I wanted to chat with you about a cool way to configure LiveKit with Deepgram for barge-in handling. It's totally possible, and I'll break down how you can do this. - Configuration: Think of this like setting up a smart conversation flow. By using 
turn_detection="stt"
and Deepgram as your STT provider, you're tapping into some neat built-in features like 
speech_final
events that help manage conversation turns. - Omitting VAD: LiveKit usually recommends including a VAD plugin, but don't worry - the system will still work without it. You might see a little warning, but everything will run smoothly using just the STT endpointing signals. Now, let's talk about a few things to keep in mind: 1. Reduced Responsiveness: Just a heads up, going this route might make interruption handling a bit less snappy. LiveKit's VAD models are pretty awesome at making conversations feel more natural. 2. Recommendation: The LiveKit team suggests using a VAD plugin alongside STT endpointing for the smoothest experience. It's like having a really attentive conversation partner! If you're thinking of going this route, just make sure to fine-tune Deepgram's endpointing parameters to match exactly what you need. Feel free to reach out if you want to chat more about making this work perfectly for you! Sources: Turn detection and interruptions | LiveKit Docs | livekit-agents/livekit/agents/voice/agent_session.py
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