Need some help troubleshooting 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 in Singapore (SIP trunk ID: byoc-sg-02). The primary carrier shows stable 95% accuracy, while the secondary drops to 68% during peak hours (10:00 SGT - 14:00 SGT).
The audio quality metrics (MOS score) remain acceptable (4.2), so packet loss isn’t the obvious culprit. However, the latency jitter on this trunk averages 120ms higher than the primary. I suspect the speech-to-text engine might be struggling with the timing gaps, causing word boundary errors that confuse the NLP layer.
Here is the relevant routing configuration:
trunk_routing:
primary:
carrier: telstra_apac
failover_threshold: 3
secondary:
carrier: singtel_byoc
latency_compensation: false # Currently disabled
codec_priority:
- opus
- g711u
Has anyone observed similar NLP degradation correlated with SIP jitter on BYOC connections? Should we enable latency compensation or adjust the STT buffer settings in Architect?
Check your media server timeout settings for those specific trunks. High latency often causes packet loss that mimics confidence drift, so review the carrier-specific tuning guide here: https://support.example.com/article/12345.
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. When high-latency carriers introduce jitter, the default buffer may flush incomplete phoneme data, causing the NLP engine to process fragmented audio segments. This results in artificially low confidence scores, even if the underlying speech recognition is accurate.
The documentation suggests increasing the voice_transcription_buffer_size for specific BYOC trunks. This can be configured via the Admin Console under Voice > Trunks > [Trunk Name] > Advanced Settings. Locate the Transcription Buffer parameter and adjust it from the default 200ms to 400ms or 500ms. This provides additional time for the media server to aggregate packets before sending them to the transcription service.
Additionally, review the max_jitter_buffer setting. If this value is too aggressive, it may drop late-arriving packets that contain critical context for intent classification. A conservative approach is to set max_jitter_buffer to 100ms for high-latency carriers. This change does not require a flow redesign but should be tested in a non-production environment first.
Monitor the Transcription Latency metric in the Performance Dashboard after applying these changes. A reduction in latency variance often correlates with improved NLP confidence. If the issue persists, consider enabling adaptive_buffering if your tenant has access to that feature. This allows the platform to dynamically adjust buffer sizes based on real-time network conditions.
This approach addresses the root cause without altering the carrier selection logic. It also maintains compliance with tenant isolation policies, as these settings are applied at the trunk level rather than globally. The previous suggestion regarding media server timeouts is valid, but buffer adjustments often yield faster improvements in confidence scoring for latency-sensitive environments.
I normally fix this by ensuring the recording export jobs include full chain-of-custody metadata, as fragmented audio often corrupts the legal hold audit trail. Warning: Verify your S3 integration permissions before adjusting buffer settings to prevent data loss.