Thanks — I’m responding to your three questions about improving DOB transcription over SIP with RealtimeModel + Azure, semantic_vad support, and per-agent STT overrides. 1) Improving transcription accuracy for spoken dates over SIP (8 kHz, telephony) • Use a dedicated high-accuracy STT for the DOB step instead of relying on the realtime model’s internal transcription. For telephony, an STT tuned for telephony audio (e.g., Azure’s speech STT or

gpt-4o-transcribe

when available as an STT plugin) gives better results for structured inputs like dates. • Tune server VAD (see next section) to avoid premature cutoffs: increase

silence_duration_ms

(try 1000–2000 ms) and set

threshold

conservatively so the VAD doesn’t cut between date segments. • Prefer a single-shot capture flow for DOB: ask the user to say the full DOB in one phrase and give explicit examples/format prompts (e.g., “Please say your birth month, day, and year, for example: January fifteenth nineteen eighty”). That helps the STT map multi-part utterances. • Keep noise reduction and telephony noise-cancellation (BVCTelephony) — these are appropriate for SIP. Ensure your SIP codec and transport preserve speech frequencies as much as possible (8 kHz is limited but typical for telephony). • If you still see errors, collect STT confidence scores and add a quick repeat/confirmation step: transcribe → prompt back the parsed date for confirmation → if confidence low, re-prompt with clearer guidance. 2) semantic_vad support and whether it causes premature cutoffs • semantic_vad is not supported on Azure OpenAI Realtime. The system uses Server VAD. • If you configure

SemanticVad(...)

, it will not provide true semantic VAD behavior on Azure and may effectively fall back to Server VAD settings. This can cause premature turn cutoffs for multi-part inputs like dates. • Actionable settings to avoid premature cutoffs: increase

silence_duration_ms

(1000–2000 ms), adjust

threshold

upward to ignore background noise, and increase

prefix_padding_ms

if you want more pre-speech audio preserved. 3) Overriding the STT model for a single agent in a multi-agent workflow • You can use a separate STT plugin for specific steps/agents. It’s possible to configure an

AgentSession

to use a different STT instance for that agent (for example, switching to a higher-accuracy STT like

gpt-4o-transcribe

or Azure STT during the identity verification step). • This pattern works alongside a Realtime model session: keep the RealtimeModel for the agent’s reasoning/response, and attach/route STT output from the separate STT plugin to the agent for that step. • Note: Azure Realtime does not expose semantic_vad and realtime models may not produce interim transcripts — relying on the external STT plugin for real-time, accurate transcripts is the recommended approach for structured captures like DOB. If you’d like, I can: • Suggest exact Server VAD parameter values to try based on your retry behavior (share sample failure timings), or • Provide a configuration snippet showing how to attach a telephony-tuned STT plugin for just the DOB agent step. Which would you prefer? Sources: OpenAI Realtime API plugin guide | LiveKit Documentation | Azure OpenAI Realtime API and LiveKit | LiveKit Documentation | Azure OpenAI STT plugin guide | LiveKit Documentation | Realtime models overview | LiveKit Documentation | LiveKit turn detector plugin | LiveKit Documentation, with interactive elements