Dynamic Endpointing For AI Assistants
Creating an AI Assistant that effectively handles user communication involves managing dynamic endpointing settings. Endpointing refers to the ability to detect pauses or the end of a user's speech, which is critical when tailoring an AI system that responds swiftly while handling user interruptions or prolonged inputs like phone numbers or IDs.
In scenarios where a low endpointing setting (such as 0ms) is preferred for snappy responses and quick interruption detection, there might be a necessity to adjust this setting. Specifically, when a user needs to input longer sequences of speech, like a phone number or an insurance ID, a higher endpointing threshold is beneficial to avoid premature speech termination.
Adjusting Endpointing Settings
The Deepgram API does not currently support dynamically altering endpointing settings without disconnecting and reconnecting the WebSocket. However, if you're buffering audio, a viable workaround is to manage the connection lifecycle—disconnecting and reconnecting—when you need to adjust these settings.
Considerations
- Buffering Audio: For handling changes in endpointing settings, buffering audio can assist in maintaining conversation continuity. This involves temporarily holding audio data during connection adjustments.
- Connection Management: Given that Deepgram allows up to 100 concurrent connections on a standard contract, managing multiple connections for different settings might be an option if fine-tuning real-time adjustments is needed.
For more support or to dive deeper into endpointing options, consider visiting our Discord or joining the discussions on GitHub Discussions.
Conclusion
Dynamic endpointing in AI applications helps deliver a smoother experience by adapting to different conversational contexts. While direct support for dynamic adjustments within a single connection is limited, strategic use of connection management and audio buffering can achieve desired outcomes in many setups.