Issue

Customers using Deepgram STT with ConversationRelay are experiencing false positives where the system incorrectly identifies the caller's language. This causes the language tags to flip mid-conversation, leading the LLM to unexpectedly switch the response language.

Product

Conversations

Environment

Twilio Console

Cause

When auto-detection is enabled with a large list of candidate languages, short utterances or background noise can be misinterpreted as words in a non-active language. For example, the system may mistake an English sound for a similar-sounding word in another language, causing the engine to rapidly update the language tag and trigger an incorrect LLM response.

Resolution

To stabilize your setup and minimize language-switching errors, implement the following adjustments:

• Reduce the Number of Candidate Languages: Trim your TwiML list down to only the highest-demand languages for your specific use case. Evaluating against too many options increases the margin for error.
• Standardize Speech Models: Ensure all <Language> blocks use the same model version (e.g., set all to nova-3-general). Mixing model versions (like nova-2 and nova-3) within the same auto-detection pool can cause routing inconsistencies.
• Deploy Region-Specific Numbers: If you have multiple Twilio numbers, assign specific numbers to regional or language-specific workflows to narrow down the expected languages.
• Utilize Programmatic Language Switching: Instead of relying on passive auto-detection, lock the initial TwiML to a default language. Use your backend logic to send a structured switch language message over the WebSocket connection only when the LLM detects a verified language change.

Additional Information

For detailed implementation steps regarding programmatic changes, please refer to the ConversationRelay WebSocket Messages documentation.