Statistical Metrics

Statistical metrics quantify timing, signal, and acoustic properties of a conversation through deterministic analysis. Nearly all run without model inference, so results are reproducible for the same input (Agent Repeats Itself is the exception — its default mode uses an LLM judge). Most operate on audio and report a numeric value with a defined unit. Audio metrics you create yourself can take an audio scope to restrict analysis to a specific speaker or time range — see scoping audio metrics. Built-in metrics ship preconfigured, with no scope settings.

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Conversation & timing

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Audio Duration

Total length of the call audio. A simple descriptive measure that anchors per-minute and percentage metrics on the same call. What it measures — The total duration of the call audio in seconds. When to use — Catching calls that run far longer than the scenario should take or that end almost immediately — both often signal a stuck dialogue, an early hangup, or a failed connection. How it works — Reads the length of the audio directly from the recording. How to interpret — A descriptive measure of call length with no inherent good direction — compare against the expected length for your scenario. Sudden shifts across runs usually trace back to a dialogue or configuration change rather than an audio problem. Configuration — None (built-in). Requires Audio · Unit seconds

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Interruption Rate

How often the agent starts speaking while the user is still talking, reported as interruptions per minute. Only agent-over-user interruptions count — times the user interrupts the agent are excluded. What it measures — Counts agent-onset-during-user-speech events across the call and normalizes them to a per-minute rate. When to use — Conversation flow analysis — e.g., verifying that a barge-in or endpointing configuration change didn’t make the agent talk over callers, or identifying turn-taking problems reported by users. How it works — Compares the agent and user speaking segments from diarized (speaker-separated) audio and flags points where the agent’s speech begins before the user’s turn ends. How to interpret — Lower is better. A low rate means the agent waits for the user to finish before responding; an elevated rate points to a barge-in or turn-taking problem worth tracing back to your endpointing settings. Configuration — None (built-in). Requires Audio · Unit interruptions per minute

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Latency

Average silence gap between the user finishing a turn and the agent starting to respond. What it measures — The average silence gap in seconds between the user finishing a turn and the agent starting to respond, counting gaps of at least 0.5 seconds. When to use — Performance evaluation across model, provider, or pipeline changes — e.g., confirming a new LLM or TTS provider didn’t slow down responses, or identifying slow turns that make conversations feel unresponsive. How it works — Reads the speaking segments detected for the call (voice activity detection runs upstream) and measures the silence between the end of each user segment and the start of the agent’s next one. How to interpret — Lower is better — lower values indicate a faster, more responsive agent. Higher values may indicate performance issues or processing bottlenecks; track across runs to catch regressions when you change models or providers. Configuration — None (built-in). Requires Audio · Unit seconds

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Speaking Time Percentage

Share of the call the configured speaker was actively talking. The roles are complementary, so multiple instances give a full picture of call composition. What it measures — The percentage of total call duration that the configured speaker (default: agent) was actively speaking. When to use — Analyzing call composition — e.g., checking whether the agent dominates the conversation, identifying excessive hold music, or measuring dead air. How it works — Sums the configured role’s speaking segments and divides by total call duration; roles (agent, persona, silence, music) are complementary and sum to 100%. How to interpret — Higher values mean the speaker held a greater share of the conversation; interpret relative to expected call composition. Create one instance per role for a full breakdown, and use the highlighted waveform regions to see where each segment falls. Configuration — Speaker scope: agent, persona, or music (silence appears in the breakdown but is not selectable); default agent. Requires Audio · Unit percent · Scope Audio

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Time to First Audio

How quickly the conversation produces its first audible sound. What it measures — The time in milliseconds from the start of the recording until the first audible sound is detected. When to use — Evaluating system or agent latency before any speech begins — e.g., measuring how long callers wait before a telephony agent picks up and greets them. How it works — Detects the first audio frame with RMS energy (signal level) above a threshold and returns its timestamp. How to interpret — Lower is better — lower values indicate a faster start. Under 1000 ms is responsive, 1–3 s is acceptable, and above 3000 ms is noticeable lag. A value of -1 ms means no audio was detected — likely a technical failure or silent recording. Configuration — None (built-in). Requires Audio · Unit milliseconds

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Words per Message

Average response verbosity of the agent. What it measures — The average number of words the agent used per message across the conversation. When to use — Enforcing brevity guidelines — e.g., verifying a prompt change didn’t make the agent ramble, or comparing verbosity across prompt versions. How it works — Counts words in each agent message and averages across the conversation. How to interpret — Use this to gauge response verbosity; there is no inherent good direction — compare against your length guidelines. Track across runs to catch verbosity drift after prompt changes. Configuration — None (built-in). Requires Transcript · Unit words per message

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Audio signal & prosody

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Abrupt Pitch Changes

How often the agent’s pitch jumps abruptly during speech — sudden, jittery transitions that make synthesized voices sound unnatural. What it measures — How often pitch changes abruptly between frames during speech, reported as events per minute. When to use — Identifying voice models with unstable or jittery pitch, or comparing voice configurations for smoothness before rolling one out. How it works — Compares pitch frame-by-frame, flags frames exceeding a change threshold, and groups consecutive flagged frames into segments. How to interpret — Lower is better — lower values indicate smoother, more natural-sounding delivery; higher values suggest jittery or unstable pitch. Configuration — Pitch-change threshold in Hz (significant_changes_threshold_hz, default 200). Requires Audio · Unit per minute · Scope Audio

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Audio Frequency

Share of voiced speech sitting above a pitch floor — a fingerprint of a voice model’s frequency distribution. What it measures — The percentage of voiced speech frames with a fundamental frequency above the configured threshold. When to use — Comparing voice models or speech synthesis output on frequency distribution — e.g., checking that two TTS providers render the same voice with a similar pitch profile. How it works — Detects voiced frames and computes the fraction whose fundamental frequency clears the threshold (300 Hz by default). How to interpret — Use it to check that a voice model’s frequency profile stays consistent — e.g., after a provider switch or a telephony codec change shifts the spectrum. There is no universal good direction; compare against a known-good baseline for the same voice. Configuration — Frequency threshold in Hz (default 300). The configurable variant also sets the direction (above or below). Requires Audio · Unit percent · Scope Audio

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Background Noise

Cleanliness of the call audio relative to background noise. What it measures — The signal-to-noise ratio (SNR) of the call audio in decibels — how loud the speech is relative to the background noise floor. When to use — Audio quality assessment and identifying poor recording conditions — e.g., validating telephony line quality before low SNR starts degrading transcription accuracy. How it works — Compares the overall speech level against the quietest audio windows to estimate the ratio. How to interpret — Higher values mean cleaner audio. Above 20 dB is excellent, 10–20 dB is acceptable for most applications, and below 10 dB may significantly impair speech recognition and comprehension. Configuration — None (built-in). Requires Audio · Unit decibels

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Clipping Artifact

Near-full-scale saturation in the supplied audio, which can indicate clipping. What it measures — The fraction of analyzed speech samples that belong to consecutive runs of near-full-scale saturation, reported from 0 to 1. When to use — When speech sounds distorted or crackly and you want to check for saturation in the supplied audio. How it works — Identifies consecutive samples close to the maximum audio level and reports their share of the analyzed speech. Isolated peaks do not qualify as runs. How to interpret — Lower is better. A higher fraction means more of the analyzed speech meets the saturation pattern. A low score cannot rule out distortion introduced earlier: audio that was clipped and then rescaled may no longer reach full scale. The score does not identify where clipping occurred or measure every kind of distortion. Configuration — None (built-in). Requires Audio · Unit fraction 0–1 · Scope Audio

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Codec Artifact

Spectral anomalies that can make speech sound garbled, muddy, or digitally degraded. What it measures — The mean severity of detected spectral anomaly events, reported from 0 to 1. It is a severity score, not the fraction of audio affected. When to use — When speech sounds digitally degraded even though it does not have silent gaps or near-full-scale saturation. How it works — Compares spectral characteristics within the supplied audio and scores regions that differ unusually from its typical spectral pattern. How to interpret — Lower is better. Higher scores indicate more pronounced spectral anomalies. Codec-like distortion can appear without silence or saturation, but the score does not prove a codec caused it or identify a particular codec. Compare similar audio conditions and listen to flagged regions before drawing conclusions. Configuration — Optional preset and detector-tuning overrides via metric metadata. Requires Audio · Unit severity 0–1 · Scope Audio

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Dropout Artifact

Brief, abrupt attenuation during speech that may indicate signal loss. What it measures — The density of signal-loss candidates per minute of analyzed speech: brief events where the signal level drops sharply relative to the surrounding audio. When to use — When speech sounds choppy or briefly cuts out, or when you want to investigate abrupt gaps in the waveform. How it works — Looks for a sharp drop from the surrounding signal level and checks that the gap is brief. These checks reduce ordinary silence detections, but natural pauses and phonetic closures can still resemble dropouts. How to interpret — Lower is better. Higher values mean more brief attenuation events, not proof of packet loss or a particular technical cause. Listen to flagged regions to distinguish signal loss from normal speech gaps. This metric does not measure arbitrary spectral distortion; use Codec Artifact for spectral anomalies and Pause Analysis for pause timing. Configuration — None (built-in). Requires Audio · Unit per minute · Scope Audio

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Loop Detection

Repeated agent utterances indicating a TTS or dialogue loop. What it measures — The count of distinct loop clusters where the agent repeats an utterance in a way that signals a TTS or dialogue glitch. When to use — When agents occasionally repeat themselves verbatim in a way that doesn’t match the conversation script, or when TTS audio sounds like it stuttered and replayed a segment. How it works — Computes MFCC fingerprints (compact acoustic signatures) across the audio and detects recurring segments, requiring high acoustic similarity between the repeats to distinguish true TTS loops from natural lexical repetition. How to interpret — Lower is better — more distinct loop clusters mean the agent is repeating itself more often. Any confirmed loop is worth investigating, as true loops are rarely intentional. Configuration — Optional preset and detector-tuning overrides via metric metadata (minimum loop duration, repetition count, acoustic-similarity threshold). Requires Audio · Unit count · Scope Audio

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Non-Expressive Pauses

Pauses that arrive without preceding pitch movement, which can make the agent sound flat or monotone. What it measures — How often the agent pauses without any preceding pitch movement, reported as events per minute. When to use — Evaluating whether a voice sounds expressive and natural — e.g., detecting monotone delivery in synthesized speech or comparing voice configurations for expressiveness. How it works — Detects pauses above a minimum duration and examines the pitch trajectory in the frames just before each pause, flagging those with minimal variation. How to interpret — Lower is better — lower values mean pauses are accompanied by natural inflection; higher values suggest a flat, robotic cadence where pauses arrive without natural pitch cues. Configuration — Minimum pause duration (default 0.6 s) and pre-pause window (default 5 frames). Requires Audio · Unit per minute · Scope Audio

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Pause Analysis

Pause timing and frequency during the selected speaker’s speech, with unusually long pauses flagged. What it measures — How frequently the agent pauses during speech (pauses per minute) and flags pauses that are unusually long relative to the speaker’s own baseline. When to use — Identifying frequent or unusually long hesitations and comparing speech pacing across voice configurations. Use Dropout Artifact to investigate brief, abrupt signal attenuation. How it works — Measures gaps between the selected speaker’s speech segments and flags pauses that are unusually long relative to that speaker’s typical pauses. Gaps across another speaker’s turn are excluded. How to interpret — Higher values mean more pauses per minute. Pauses can be natural and expressive, so compare with the expected pacing rather than treating every pause as a defect. Pause timing does not establish abrupt signal loss or explain the cause of a gap. Configuration — A preset (strict, normal, loose) or an explicit minimum pause duration is required; optional speaker role (default agent) and anomaly z-score threshold. Requires Audio · Unit pauses per minute · Scope Audio

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Perceived Loudness

Overall call loudness, measured as integrated loudness in LUFS. What it measures — The integrated loudness of the call audio in LUFS (ITU-R BS.1770). LUFS is an absolute loudness unit, so it is better suited than relative decibels for comparing perceived volume across runs. When to use — Catching volume regressions after changing a TTS provider, voice, codec, normalization step, or telephony path — for example, when an agent becomes noticeably quieter or louder even though the conversation logic is unchanged. How it works — For whole-call metrics, Coval reads the loudness measured during audio normalization, so the metric does not re-download or re-process the audio. For speaker-scoped metrics, Coval re-measures loudness on that speaker’s audio only. The detail view can mark loudness outlier regions on the waveform when short windows differ sharply from the clip’s integrated loudness. How to interpret — Values closer to 0 LUFS are louder; more negative values are quieter. Track against your expected baseline for the same channel and voice. Large shifts usually indicate a gain, encoding, TTS, or normalization change. Speaker-scoped results are useful when one side of the call is too quiet or too loud relative to the other. Configuration — Optional outlier sensitivity in LU and target loudness for display context. Requires Audio · Unit LUFS · Scope Audio

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Phoneme Stretch

Unnaturally sustained phonemes that signal a TTS glitch. What it measures — The total duration of phonemes held well beyond natural duration, which signal a TTS synthesis glitch. When to use — When TTS audio sounds like it is “freezing” on a syllable or vowel — common when synthesis models hit unusual input such as long numbers, rare proper nouns, or SSML edge cases. How it works — Flags regions that simultaneously satisfy voiced detection, low pitch jitter, stable fundamental frequency, and minimal spectral movement — all four must hold to exclude natural expressive lengthening. How to interpret — Lower is better. Events under 0.5 s may be expressive prosody, while events above ~1.5 s are almost certainly synthesis defects; higher total stretch duration indicates more severe TTS artifacts. Configuration — Optional preset and detector-tuning overrides via metric metadata (minimum stretch duration, voicing and jitter thresholds). Requires Audio · Unit seconds · Scope Audio

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Pitch Variability

How much the agent’s pitch moves over a call — flat and monotone versus lively and expressive. What it measures — The variation in the agent’s pitch (fundamental frequency), reported as the standard deviation of F0 in semitones. Because it is measured in semitones, the score is independent of the speaker’s vocal register, so higher- and lower-voiced agents are directly comparable. When to use — Catching robotic, monotone delivery — e.g., comparing expressiveness across TTS voices, or verifying a voice settings change didn’t flatten the agent’s intonation. How it works — Tracks the agent’s pitch across all voiced speech and measures how widely it ranges; the deep dive shows the pitch contour over time with monotone and expressive stretches highlighted, alongside the agent’s mean pitch and range in Hz for context. How to interpret — Higher values indicate more expressive, dynamic intonation; lower values indicate flatter, more monotone delivery. A very low value can signal robotic or disengaged-sounding speech; a natural conversational voice typically shows moderate variation. Configuration — None (built-in). Requires Audio · Unit semitones (st) · Scope Audio

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Sentence Intonation

How often a sentence ends on the wrong pitch for its type — statements that rise (uptalk) or yes/no questions that stay flat. What it measures — The percentage of classifiable sentences whose terminal pitch (boundary tone) is inappropriate: a statement that rises, or a yes/no question that stays flat. Higher values indicate a more robotic or uncertain-sounding delivery. When to use — Catching uptalk or flat, declarative-sounding questions in TTS output — e.g., verifying a voice sounds confident on statements and naturally inquisitive on questions. How it works — Maps each transcript sentence to an audio span (its end anchored to the largest pause between the speaker’s turns), classifies the pitch change over the final window of voiced speech as rise, fall, or flat, and compares it to the sentence type. Reports the share of sentences with a mismatched boundary tone. How to interpret — Lower is better. Higher values flag uptalk on statements or monotone yes/no questions, which read as robotic or uncertain. Sentence ends are anchored to pauses rather than word-level timestamps, so very short or run-together sentences may not be classifiable. Configuration — None. Requires Audio · Unit percent · Scope Audio

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Spectrogram Pitch Analysis

Whether the audio carries natural upper-frequency content — synthetic or bandwidth-limited audio often lacks energy in higher frequency ranges. What it measures — Whether the agent’s audio contains natural upper-frequency content across the speech spectrum. When to use — Detecting bandwidth-limited or muffled synthesized speech — e.g., comparing voice model configurations for spectral richness, or catching voices that lack harmonic upper-frequency energy. How it works — Splits the audio into windows, measures the fraction of upper-frequency bins above a noise floor in each, and passes when the average fill ratio meets the naturalness threshold. How to interpret — Returns YES when the audio passes the naturalness threshold, NO when it appears bandwidth-limited or synthetic. The detail view shows the fill ratio per window across the recording timeline. Configuration — Optional analysis overrides via metric metadata (segment length, naturalness threshold, noise floor). Requires Audio · Unit YES/NO · Scope Audio

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Speech Artifact Anomaly

A weighted composite score for tracking speech artifacts and delivery anomalies. What it measures — A composite of clipping, dropout, loop detection, phoneme stretch, syllable rate, voice quality, timbre drift, and pause checks. Codec Artifact is a separate metric and is not included. When to use — As your primary artifact health signal when you want a single number to track across runs or set an alert threshold on, drilling into the breakdown only when it degrades. How it works — Combines the supported component severities using a weighted average. Components that cannot be evaluated are excluded, and the remaining weights are adjusted. How to interpret — Scores range from 0 to 1, with higher scores indicating fewer or less severe issues across the evaluated components. Review the component breakdown and listen to affected regions when the score changes. The composite is not a classifier for codec, signal-loss, or other technical causes, and a high score does not guarantee distortion-free audio. Configuration — None (built-in). Requires Audio · Unit score 0–1 · Scope Audio

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Phonemes per Second

Agent speech rate measured in phonemes. What it measures — The agent’s speech rate as phonemes produced per second of speaking time. When to use — Tuning TTS speaking rate — e.g., verifying a provider’s speed setting lands in a comfortable range, or comparing pacing across voice models. How it works — Counts phonemes per interval over the duration of each speech segment. How to interpret — Higher values indicate faster delivery. Roughly 10–15 pps is a comfortable target; 15–20 pps is fast but can remain comprehensible, above 20 pps is hard to follow, and below 10 is too slow. Configuration — None (built-in). Requires Audio · Unit phonemes per second · Scope Audio

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Syllable Rate

Agent speaking rate in syllables, with out-of-range pacing flagged. What it measures — The agent’s speaking rate in syllables per second, flagging segments where the pace falls outside the normal range for the configured language. When to use — When agent speech sounds robotic or rushed, or to validate that a new TTS model produces natural prosodic rhythm before deploying it. How it works — Measures absolute syllable rate alongside rhythm diagnostics (nPVI, inter-syllable variation) and penalizes rates outside the natural range. How to interpret — Values near 4–5 syl/s are typical for English; the natural range spans roughly 2.5–6.0 syl/s — above it suggests rushed synthesis, below it sluggish or over-paused delivery. Examine the rate together with rhythm — uniformly low variability can indicate robotic cadence. Configuration — Language (default English), a preset, and explicit low/high rate-threshold overrides. Requires Audio · Unit syllables per second · Scope Audio

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Vocal Fry

Low, creaky vocal quality from irregular vocal cord vibration, typically at the end of phrases. What it measures — The total duration of vocal fry in the agent’s speech. When to use — Evaluating whether a voice has creaky or rough-sounding artifacts — e.g., monitoring vocal quality across voice configurations, or identifying voices where fry degrades the listener experience. How it works — Identifies frames with simultaneously low pitch, high acoustic roughness, and irregular vocal cord vibration, then groups consecutive flagged frames into fry segments. How to interpret — Lower is better — fewer seconds of fry indicate cleaner, more professional-sounding audio. Occasional brief fry is normal in natural speech; sustained or frequent fry reduces perceived quality. Configuration — Optional detector-tuning overrides via metric metadata (pitch floor and ceiling, jitter and harmonics-to-noise thresholds). Requires Audio · Unit seconds · Scope Audio

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Voice Quality

Acoustic naturalness of the voice, combining four acoustic dimensions into one score. What it measures — A composite naturalness score from clarity (CPPS, a voice-clarity measure), pitch perturbation (jitter), amplitude perturbation (shimmer), and pitch variability. When to use — Comparing voices, tracking changes in vocal naturalness, or investigating speech that sounds robotic, breathy, or unsteady. How it works — Measures clarity, small pitch and amplitude variations, and broader pitch variability, then combines them into a weighted score. How to interpret — Scores range from 0 to 1; higher is better for acoustic naturalness. Speech style and expressive delivery affect these properties, so compare similar voices and review the component scores. This is not a measure of transport integrity: a high score does not rule out cutouts, clipping, or spectral distortion. Use the corresponding artifact metrics to investigate those patterns. Configuration — None (built-in). Requires Audio · Unit score 0–1 · Scope Audio

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Volume Variance

Consistency of the agent’s volume across the call. What it measures — How consistently the agent maintains volume, reported as the standard deviation of loudness in decibels. When to use — Identifying erratic loudness changes in agent speech — e.g., ensuring consistent audio quality across a call, or comparing voice model configurations for volume stability. How it works — Divides agent speech into fixed-length intervals, measures each interval’s loudness, and reports the standard deviation across intervals. How to interpret — Lower is better — lower values indicate steadier, more even audio output. The detail view shows only the problematic intervals (too loud or too soft) with their timestamps and dB values. Configuration — Loud/soft threshold preset (strict, normal, lenient) or individual dB and interval overrides. Requires Audio · Unit decibels · Scope Audio

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Volume-Pitch Misalignment

Moments where pitch and volume move in opposite directions — for example, a voice getting louder while its pitch unexpectedly drops. What it measures — How often pitch and volume move in opposite directions during speech, reported as mean misalignment severity. When to use — Evaluating text-to-speech engine quality — e.g., detecting prosody issues that sound “off” to listeners, or comparing voice model configurations. How it works — Analyzes frame-by-frame pitch and volume changes, flags divergent frames, and scores each by how unusual the divergence is relative to the clip’s own baseline (z-scored). How to interpret — Lower is better — lower values indicate better prosodic consistency. Severity around 0–1 is unremarkable, 1–2 means one or both signals sit about a standard deviation above the clip mean, and 2+ marks genuinely unusual frames. Because severity is relative to the clip, scores are comparable across speakers and recordings. Configuration — Minimum intensity change to flag an event (min_volume_change_for_pitch_misalignment, default 7 dB). Requires Audio · Unit severity · Scope Audio

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Agent behavior

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Agent Repeats Itself

Whether the agent repeated the same phrases or questions. What it measures — Whether the agent repeated the same phrases or questions multiple times in a conversation. When to use — Evaluating naturalness and language diversity — e.g., catching an agent that re-asks a question it already asked, or falls back on the same stock phrase when it gets stuck. How it works — By default an LLM judge reviews the transcript for problematic repetition; a regex mode is available for fully deterministic pattern-based detection. How to interpret — Returns YES if problematic repetition was found, NO otherwise. NO is the good direction — diverse, non-repetitive language sounds more natural. Configuration — Detection mode: llm_judge (default; optional custom prompt and judge model) or regex (pattern, similarity threshold, minimum repetition count, case sensitivity). Requires Transcript · Unit YES/NO · Scope Transcript