> ## Documentation Index
> Fetch the complete documentation index at: https://docs.roark.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# System Metrics Reference
> Complete reference for all built-in system metrics powered by specialized models
Roark ships with a comprehensive set of **system metrics** that are automatically available in every project. These metrics are powered by purpose-built, specialized models — not generic LLMs — designed to extract precise signal from conversational audio and transcripts.
System metrics require no configuration. Add them to an [analysis package](/documentation/metrics/custom-metrics), attach a [metric policy](/documentation/metrics/metric-policies), and start collecting data immediately.
All system metrics listed below are powered by **specialized models** purpose-built for voice AI analysis. This means they are faster, more consistent, and more cost-effective than general-purpose LLM evaluation.
***
## Metric Types Overview
Roark supports four ways to define metrics. System metrics use the first type, and you can create your own using any of the four:
| Type | How it works | Use case |
| :------------------------------ | :---------------------------------------------------------------------------- | :----------------------------------------------------------------- |
| **System (Specialized Models)** | Purpose-built models analyze audio and transcript signals | Performance, interruptions, sentiment, compliance, call quality |
| **LLM as Judge** | An LLM evaluates the conversation against a natural-language prompt you write | Custom business logic, subjective quality checks, task completion |
| **Pattern** | Regex or keyword matching against transcript text | Detecting specific phrases, prohibited words, required disclosures |
| **Formula** | Combine existing metrics using boolean logic and weighted expressions | Composite scores, pass/fail rules based on multiple metrics |
### LLM as Judge
Define a metric with a natural-language prompt. **Roark Prism** — our evaluation model optimized for voice AI — scores each call against your prompt and returns a typed result (boolean, scale, classification, count, etc.).
```
"Did the agent verify the caller's identity before proceeding?" → Boolean
"Rate the agent's empathy on a 1-5 scale" → Scale
"What was the primary call reason?" → Classification
```
Create LLM as Judge metrics in the [dashboard](/documentation/metrics/custom-metrics) or via the [SDK](/documentation/metrics/custom-metrics#create-a-metric-definition).
### Pattern Detection
Match specific patterns in the transcript using keywords or regex. Useful for detecting required phrases, prohibited language, or specific conversational markers without LLM overhead.
### Formula Metrics
Combine multiple metrics into a single composite score using boolean logic and weighted expressions. For example, define a "Call Success" metric that requires `frustration_score < 3 AND instruction_follow = TRUE`.
Formula metrics let you build layered quality gates from your existing metrics without writing any code. Learn more about creating metrics in [Custom Metrics](/documentation/metrics/custom-metrics).
***
## System Metrics Reference
All system metrics below are collected automatically when included in an analysis package. Each metric shows its output type, scope, and the specialized model that powers it.
**Scope legend:**
* **Global** — one value per call
* **Per-participant** — separate values for agent and customer
***
### Core Analysis
Timing and interaction metrics extracted from audio diarization and transcript alignment.
Powered by **Roark Vibe** — our core voice analysis model.
| Metric | Description | Output | Scope |
| :--------------------- | :---------------------------------------------------------------- | :---------------- | :-------------- |
| `call_duration` | Total duration of the call | Numeric (seconds) | Global |
| `response_time` | Time between speaking turns | Numeric (seconds) | Per-participant |
| `time_to_first_word` | Time from call start to first spoken word | Numeric (seconds) | Per-participant |
| `silence_duration` | Duration of each silence period | Numeric (seconds) | Per-participant |
| `turn_duration` | Duration of each speaking turn | Numeric (seconds) | Per-participant |
| `word_count` | Number of words spoken | Count | Per-participant |
| `talk_to_listen_ratio` | Ratio of time a participant spends talking vs total call duration | Numeric | Per-participant |
| `speaking_rate` | Words spoken per minute by a participant | Numeric (wpm) | Per-participant |
| `turn_count` | Number of speaking turns by a participant | Count | Per-participant |
| `latency_spike_count` | Number of response gaps exceeding 3 seconds | Count | Per-participant |
| `longest_pause` | Longest gap between consecutive segments in the call | Numeric (seconds) | Global |
***
### Sentiment & Emotion
Emotion and sentiment analysis from vocal features and acoustic signals.
Powered by **Hume Expression Measurement** — a specialized vocal emotion model.
| Metric | Description | Output | Scope |
| :----------------- | :----------------------------------------------------------- | :------------- | :-------------- |
| `sentiment_score` | Sentiment rating on a 1–9 scale (1 = negative, 9 = positive) | Scale (1-9) | Per-participant |
| `emotion_label` | Detected emotion label from 64+ emotions | Classification | Per-participant |
| `dominant_emotion` | Most frequent emotion across the call | Classification | Per-participant |
| `vocal_cue_label` | Detected vocal cue or expression label | Classification | Per-participant |
***
### Interruptions
Detailed interruption and overlap analysis from speaker diarization.
Powered by **Roark Interruptions** — a specialized overlap detection model.
| Metric | Description | Output | Scope |
| :----------------------------------- | :----------------------------------------------------------------------------- | :---------------- | :-------------- |
| `interruption` | Whether overlapping speech occurred on a segment | Boolean | Per-participant |
| `interruption_duration` | Duration of overlapping speech | Numeric (seconds) | Per-participant |
| `interruption_count` | Total number of interruptions | Count | Global |
| `first_interruption_time` | Time into call of first interruption | Offset (seconds) | Global |
| `overtalk_ratio` | Ratio of overlapping speech duration to total call duration | Numeric | Global |
| `agent_interruption_count` | Number of times the agent interrupted the customer | Count | Global |
| `incorrect_agent_interruption_count` | Number of agent interruptions classified as inappropriate | Count | Global |
| `incorrect_interruption_rate` | Proportion of agent interruptions that were inappropriate | Scale (0-1) | Global |
| `customer_barge_in_count` | Number of times the customer attempted to interrupt the agent | Count | Global |
| `failed_barge_in` | Whether a customer interruption attempt failed because the agent did not yield | Boolean | Per-participant |
| `failed_barge_in_count` | Number of customer interruption attempts where the agent did not yield | Count | Global |
| `failed_barge_in_rate` | Proportion of customer interruption attempts that failed | Scale (0-1) | Global |
| `interruption_appropriateness` | Whether an agent interruption was appropriate based on conversational context | Boolean | Per-participant |
| `pre_interruption_speaker_duration` | How long the interrupted speaker had been talking before being interrupted | Numeric (seconds) | Per-participant |
| `agent_cutoff` | Whether the agent started speaking while the customer was mid-sentence | Boolean | Per-participant |
| `agent_cutoff_count` | Number of times the agent cut off the customer mid-sentence | Count | Global |
***
### Quality
Experience quality scoring from conversational signals.
Powered by **Roark Quality Analysis** and **Roark Prism** — specialized models for quality assessment.
| Metric | Description | Output | Scope |
| :---------------------------- | :------------------------------------------------------------------------------------------------- | :------------- | :-------------- |
| `frustration_score` | Customer frustration level (1 = none, 5 = severe) | Scale (1-5) | Per-participant |
| `user_effort_score` | How much effort the customer exerted to accomplish their goal (1 = effortless, 5 = very difficult) | Scale (1-5) | Per-participant |
| `call_outcome` | Overall outcome: Resolved, Unresolved, Escalated, Dropped, or Follow-up Required | Classification | Global |
| `instruction_follow` | How well the agent followed its given instructions (1 = not followed, 5 = fully followed) | Scale (1-5) | Global |
| `redundant_question_count` | Questions where the agent asked for information already provided | Count | Global |
| `missed_response_count` | Moments where a participant should have responded but did not | Count | Per-participant |
| `comprehension_failure` | Whether the agent misunderstood what the customer said | Boolean | Per-participant |
| `comprehension_failure_count` | Number of times the agent misunderstood the customer | Count | Global |
***
### Repetition Detection
Conversational loop and repetition analysis.
Powered by **Roark Prism** — our evaluation model optimized for voice AI.
| Metric | Description | Output | Scope |
| :------------------- | :----------------------------------------------- | :------ | :-------------- |
| `repetition_density` | Ratio of repeated turns to total turns (0–1) | Numeric | Per-participant |
| `loop_count` | Number of distinct conversational loops detected | Count | Per-participant |
***
### Tool Invocations
Analysis of function/tool calling behavior during conversations.
Powered by **Roark Vibe** and **Roark Prism**.
| Metric | Description | Output | Scope |
| :----------------------------------- | :------------------------------------------------------------------- | :------ | :----- |
| `tool_invocation_count` | Total number of tool/function calls made during the conversation | Count | Global |
| `tool_invocation_correct` | Whether the agent invoked the correct tools at the appropriate times | Boolean | Global |
| `tool_invocation_order_correct` | Whether tools were called in the correct logical sequence | Boolean | Global |
| `tool_invocation_parameters_correct` | Whether correct parameters were passed to each tool invocation | Boolean | Global |
| `tool_invocation_result_correct` | Whether the agent correctly interpreted and used tool results | Boolean | Global |
***
### Compliance
Regulatory and safety evaluation metrics for AI agent conversations.
Powered by **Roark Prism** — customizable with your own compliance requirements.
| Metric | Description | Output | Scope |
| :--------------------------------------- | :--------------------------------------------------------------------------------------------------------- | :---------- | :----- |
| `compliance_disclosure_completeness` | Whether all required disclosures were delivered (recording notice, AI identity, licensing) | Scale (1-5) | Global |
| `compliance_prohibited_language` | Whether the agent used prohibited language (unauthorized guarantees, medical/legal advice, discrimination) | Boolean | Global |
| `compliance_pii_handling` | How properly the agent handled personally identifiable information | Scale (1-5) | Global |
| `compliance_consent_collection` | Whether required consent was obtained before data collection or recording | Boolean | Global |
| `compliance_escalation_adherence` | Whether the agent properly escalated to a human when required | Boolean | Global |
| `compliance_scope_adherence` | Whether the agent stayed within its defined scope of topics | Scale (1-5) | Global |
| `compliance_prompt_injection_resistance` | Whether the agent resisted attempts to override its instructions or jailbreak | Boolean | Global |
| `compliance_identity_consistency` | Whether the agent maintained its assigned identity and disclosed its AI nature | Boolean | Global |
| `compliance_hallucination_boundary` | Whether the agent avoided fabricating information and deferred when unsure | Scale (1-5) | Global |
***
### Voicemail Detection
Voicemail detection and handling quality assessment.
Powered by **Roark Prism**.
| Metric | Description | Output | Scope |
| :----------------------------- | :---------------------------------------------------------------------------- | :---------- | :----- |
| `voicemail_detected` | Whether the call reached a voicemail system rather than a live person | Boolean | Global |
| `voicemail_agent_left_message` | Whether the agent left a voicemail message | Boolean | Global |
| `voicemail_handling_score` | Quality of voicemail handling (beep detection, message clarity, completeness) | Scale (1-5) | Global |
***
### Accent Detection
English accent identification from audio signals.
Powered by **Roark Accent ID** — a specialized accent classification model supporting 16 English accent variants.
| Metric | Description | Output | Scope |
| :----------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | :------------- | :-------------- |
| `accent` | Detected English accent (US, British, Australian, Canadian, Indian, Irish, Scottish, Welsh, African, New Zealand, Hong Kong, Malaysian, Philippine, Singaporean, Bermudian, South Atlantic) | Classification | Per-participant |
| `accent_stability` | How stable the detected accent is across segments (1.0 = consistent, lower = varies) | Numeric (0-1) | Per-participant |
For a detailed walkthrough on using accent metrics, see the [Accent Detection recipe](/documentation/recipes/accent-detection).
***
### Call Quality (DNSMOS)
Speech quality assessment using the ITU-T P.808/P.835 Mean Opinion Score (MOS) scale.
Powered by **Roark DNSMOS** — a specialized speech quality model based on the ITU-T standard.
| Metric | Description | Output | Scope |
| :-------------------------- | :-------------------------------------------------------------------------------------------------- | :---------- | :----- |
| `speech_quality_overall` | Overall perceived speech quality (P.835 OVRL). Combines signal and background noise quality. | Scale (1-5) | Global |
| `speech_quality_signal` | Quality of the speech signal itself (P.835 SIG). Measures distortion, codec artifacts, and clarity. | Scale (1-5) | Global |
| `speech_quality_background` | Background environment quality (P.835 BAK). Higher = cleaner background. | Scale (1-5) | Global |
| `speech_quality_mos` | ITU-T P.808 Mean Opinion Score. Single overall quality rating from the audio signal. | Scale (1-5) | Global |
***
## What's Next
Create custom LLM as Judge, Pattern, and Formula metrics
Test metrics interactively against real calls
Automate metric collection with conditions-based rules
Define pass/fail criteria for your metrics