Looking for advice on an anomaly in our bot analytics reporting. We manage 15 BYOC trunks across APAC, and recently noticed a significant drop in NLP intent confidence scores specifically for calls routed through our secondary carrier with higher latency. The SIP registration is stable, but the transcription delay seems to impact the real-time NLP processing window. Any insights on tuning the timeout thresholds for these specific trunk configurations?
Thanks for the help.
Have you tried adjusting the transcription buffer settings within the Voice API configuration rather than modifying SIP-level timeouts? The issue likely stems from the platform’s default expectation of low-latency audio streams, which causes the NLP engine to finalize intent prediction before all delayed audio packets arrive.
{
"transcription_settings": {
"interim_results_enabled": true,
"final_results_timeout_ms": 5000,
"max_audio_delay_ms": 3000
},
"nlp_processing": {
"wait_for_final_transcript": true,
"confidence_threshold": 0.75
}
}
This configuration explicitly instructs the platform to wait for the final transcript rather than relying on interim results, which are often incomplete during high-latency scenarios. By increasing the final_results_timeout_ms to 5000, you provide additional time for the carrier’s audio stream to stabilize. The max_audio_delay_ms parameter helps the system recognize when a packet delay is significant enough to warrant a processing pause.
From a dashboard perspective, this adjustment should stabilize the “Intent Accuracy” metric in the Performance Views. Previously, the system was likely logging partial intents as failures, skewing the analytics. This approach avoids complex carrier-side negotiations and keeps the logic within the Genesys Cloud control plane. Ensure that the wait_for_final_transcript flag is enabled, as this is the critical switch that prevents premature NLP execution. Monitor the “Average Handling Time” metric closely after this change, as the increased wait time may slightly elevate AHT, but the gain in intent confidence will likely offset this operational cost.
This looks like a load test artifact rather than a SIP issue. Increase the final_results_timeout_ms to account for the jitter buffer, but verify your JMeter thread group isn’t saturating the WebSocket limit first.
"final_results_timeout_ms": 8000