Overview#
The Audio Intelligence module continuously analyses the audio stream of live emergency calls to detect life-threatening sounds, physiological distress indicators, and caller state signals that are not captured in the spoken transcript. Operating in real time alongside the AI dispatcher conversation, it identifies agonal breathing patterns, acoustic events such as gunshots, screams, and alarms, and voice biometric markers including stress, age range, and potential intoxication, automatically escalating to dispatchers and flagging alerts on the operations dashboard without interrupting the call.
The module addresses a critical gap in text-based emergency AI: transcripts only capture what is said, not the acoustic environment around the caller. Audio Intelligence surfaces what is heard, but not spoken, so it can be acted upon.
Alongside acoustic detection, the module streams live call intelligence to every watching console: transcript segments as they are spoken, AI classification updates, the matched response protocol, and a rolling AI-generated call brief. Dispatchers and supervisors share the same real-time understanding of a call as it unfolds, without refreshing their screens.
Key Features#
- Agonal Breathing Detection: Identifies the distinctive interrupted gasping pattern associated with cardiac arrest and severe trauma, triggering an immediate P1 priority escalation and alerting the dispatcher to begin CPR coaching even before the caller can describe the situation
- Acoustic Event Detection: Classifies environmental audio events including gunshots, screams, breaking glass, alarms, and vehicle collision sounds, providing dispatchers with scene context beyond the caller's verbal account
- Voice Stress Analysis: Detects elevated physiological stress markers in the caller's voice, supporting triage decisions where a caller is describing a situation calmly but audible stress indicators suggest greater urgency
- Age Estimation: Provides a broad age-range estimate from voice characteristics, helping dispatchers identify when a caller is likely a child or elderly person and apply appropriate escalation and communication protocols
- Intoxication Indicators: Flags speech patterns consistent with intoxication to inform dispatcher judgement and support appropriate resource allocation, without overriding the dispatcher's assessment
- P1 Auto-Escalation: Critical detections (agonal breathing, gunshots, screams) automatically raise incident priority to P1 and raise an audio intelligence alert on the dispatcher dashboard, reducing the risk that a critical signal goes unnoticed in a high-call-volume environment
- Parallel Audio Analysis: Runs independently of the AI conversation pipeline so acoustic analysis never introduces latency into the dispatcher's spoken exchange with the caller
- Confidence-Scored Alerts: Each detection includes a confidence score; alerts below threshold are surfaced as advisory signals rather than auto-escalations, reducing false positive noise for dispatchers
- Live Transcript Streaming: Broadcasts transcript segments to every subscribed console as the conversation happens, so dispatchers and supervisors follow the call in real time without refreshing
- Streaming AI Classification Updates: Pushes call priority, emergency type, detected keywords, urgency score, confidence, and a timestamp to watching consoles the moment a call is classified or reclassified
- Matched Response Protocol: Surfaces the automatically matched standard operating procedure alongside each classification, so call takers see the recommended protocol the instant the AI recognises the emergency type
- Live Call Intelligence Brief: Presents a rolling AI-generated summary of the ongoing call in the operator's call packet, updated as the conversation progresses, with the original-language version available and the latest caller statement surfaced directly in the packet
- Language and Transcript Status: Shows the detected caller language and live translation status alongside the brief, with a transcript completeness indicator so operators know how much of the call has been captured
Use Cases#
Unresponsive Caller with Agonal Breathing#
A caller dials 999 or 911 but cannot speak. The AI dispatcher hears laboured, interrupted breathing consistent with cardiac arrest. Audio Intelligence triggers an immediate P1 escalation, dispatches emergency medical units, and prompts the AI to begin guided CPR instructions for any bystanders present, before a single word has been spoken by the caller.
Active Threat Identification#
During a call reporting a disturbance, acoustic event detection identifies what sounds consistent with a gunshot. The dispatcher receives an audio intelligence alert, enabling reassessment of the incident type from public disturbance to active threat and dispatch of appropriate resources.
Child Caller Recognition#
Age estimation detects a caller is likely a child. The system flags this for the dispatcher and the AI shifts to a calmer, simpler communication style suited to a young or distressed caller, while applying child-vulnerability escalation protocols.
Panic vs Calm Discrepancy#
A caller describes what sounds like a minor situation in composed language, but voice stress analysis detects significant physiological stress markers. The dispatcher is alerted to the discrepancy, prompting more careful probing before downgrading incident priority.
Scene Awareness During Vehicle Incidents#
During a road traffic collision call, acoustic detection identifies alarm sounds and airbag deployment patterns in the background, providing scene context that supports the dispatcher's resource allocation before units arrive to confirm the situation.
Supervisor Monitoring of High-Risk Calls#
A supervisor watches a high-risk call live from their own console, following the streaming transcript and classification updates as they arrive. When the urgency score spikes, the supervisor intervenes immediately rather than learning about the escalation after the call has ended.
Protocol Pushed as the Emergency Is Recognised#
As the AI classifies a call, the matched standard operating procedure is pushed to the call taker alongside the classification. The right protocol card appears the moment the emergency type is recognised, without the call taker searching a procedure library mid-call.
Joining a Call in Progress#
A supervisor joins an ongoing call and reads the rolling AI brief, the latest caller statement, and the transcript completeness indicator instead of asking the caller to repeat details. On a call in another language, the detected language and live translation status remain visible throughout.
How It Works#
Integration#
Audio Intelligence operates as a parallel processing track within the Voice AI session, receiving the same audio stream as the speech-to-text transcription pipeline without intercepting or delaying it. Detections are published as structured events to the dispatcher operations dashboard and can trigger automatic incident priority changes through the Incident Management module. Audio intelligence alerts appear in the live call panel alongside the AI conversation transcript, giving dispatchers a unified view of spoken content and acoustic signals. Streaming transcript segments, classification updates, the matched response protocol, and the rolling call brief are delivered to every subscribed console over the platform's live subscription channels, so watching screens update as the call unfolds without a refresh. The brief also flows automatically into downstream workflows, including call transfers and after-call wrap-up, so situational context follows the call rather than being retyped. Alert thresholds and enabled detection categories are configurable per tenant.
Open Standards#
- NENA i3 (NENA-STA-010.3 / NENA-STA-021): Detections are published as audio intelligence alert events within the NENA i3 audit and incident framework, and critical detections trigger priority changes on the NENA EIDO-structured incident record.
- IETF RFC 7865 / RFC 7866 (SIPREC): The audio stream is received from the Session Border Controller via a SIPREC session in which the platform acts as the Session Recording Server (SRS); RFC 7865 carries the recording metadata XML and RFC 7866 governs the SRS role.
- ITU-T G.711 / Opus (RFC 6716): The SIPREC media sink accepts G.711 µ-law and A-law as well as Opus audio; the audio analysis pipeline consumes 16-bit linear PCM decoded from these codecs.
- IETF RFC 7852 / NENA-STA-012 (Additional Data Related to an Emergency Call): Acoustic detection results, confidence scores, and biometric indicators such as voice stress scores and age-range estimates are appended as structured additional-data blocks on the in-progress emergency call, keeping acoustic context attached to the call audit trail for post-incident review.
- ISO 639-1 (Language Codes): The parallel speech-to-text transcription pipeline, which runs alongside audio intelligence on the same stream, tags transcript segments with ISO 639-1 language identifiers to support multilingual call handling.
- IETF RFC 6455 (WebSocket): Dispatcher dashboard alerts, including audio intelligence alerts and P1 escalation notifications, together with streaming transcript segments and classification updates, are delivered in real time over tenant-scoped subscription channels carried on the WebSocket protocol.
Compliance#
- Audio analysis is performed only on active emergency calls within the answering organisation's scope
- Biometric voice analysis data (stress indicators, age estimation) is used for triage support only and is not stored as a permanent profile attribute
- Detection events and their confidence scores are recorded in the call audit trail for post-incident review and quality assurance
- Alert thresholds can be tuned by supervisors to balance sensitivity against false positive rates appropriate for each deployment context
Last Reviewed: 2026-07-16 Last Updated: 2026-07-16