Voice analytics

This document covers voice channel monitoring in Analytics and Insights: call quality scoring, speech recognition accuracy, end-to-end latency tracking, barge-in rates, and DTMF fallback metrics for voice-enabled agents.

Navigation: Project → Insights → Voice Analytics

Date range selector: Use the toggle to select Today, 7d, or 30d.

Filters: Select All environments at the top of the page to narrow the data by deployment environment. Filter selections are reflected in the page URL, so you can bookmark a filtered view or share it with a colleague.

Channel filtering isn’t available on this page. The underlying voice data source doesn’t carry a channel dimension, so the channel selector is hidden here even though it appears on other analytics pages.

KPI metric cards

Metric	Description
Total Calls	Number of voice calls in the selected period.
Avg MOS	Average Mean Opinion Score for call quality on a scale of 1–5. Scores below 3.5 typically indicate noticeable quality issues.
ASR Quality	Automatic Speech Recognition quality score (0–100, higher is better). Measures how accurately the ASR engine transcribes the caller’s speech.
E2E Latency	End-to-end latency in milliseconds for the voice processing pipeline. Covers the full round-trip from user speech input through ASR transcription, LLM processing, and TTS output back to the caller.
Barge-In Rate	Percentage of calls where the caller interrupted the agent mid-response. A rising barge-in rate may indicate that responses are too long or latency is too high, prompting callers to cut in.
DTMF Fallback	Percentage of calls falling back to touch-tone (keypad) input, typically when ASR fails to understand the caller. A rising rate may indicate ASR quality issues or unsupported accents/languages.

Trend charts

Chart	Description
Network Quality and Call Volume	Dual-axis chart plotting MOS scores and call count trends over the selected period. Use this to correlate call quality dips with volume spikes — quality often degrades during peak traffic if capacity-constrained infrastructure cannot keep up.
Speech Recognition Quality (ASR)	ASR quality scores plotted over time. Monitor for sustained degradation, which may indicate noisy caller environments, model drift, or the introduction of new vocabulary that the ASR model does not yet recognize.

Track E2E Latency trends after model or pipeline changes. Even small latency increases (50–100ms) can affect caller experience and drive up barge-in rates.