Principal-speaker gating for airport and cabin voice AI
Addressee Detection for Aviation Voice AI
Your gate kiosk fires on the PA announcement. Your cabin assistant answers the passenger in the next seat.
SAA (Selective Auditory Attention) decides, before speech-to-text, whether your gate, check-in, or cabin device is being addressed.
The failure
The gate PA. The next kiosk. The seat next door. Your voice AI answers all of them.
Each false trigger costs you: an interrupted transaction, inflated handle time, and no record of what caused it.
Better noise cancellation or beamforming will not fix this. Neither one decides who is being addressed before ASR runs.
false trigger / operational cost
The PA calls a group; the kiosk responds
A boarding announcement at 85 decibels reaches the gate kiosk microphone. The ASR transcribes it as potential user input; the voice agent fires mid-transaction.
The passenger's interaction is interrupted, handle time inflates, and there is no record of what triggered the wrong response. The recognizer is working correctly.
wrong-passenger attribution / audit risk
The seatmate speaks; the assistant answers
Cabin engine noise drops signal-to-noise ratios below 10 decibels. A passenger in the next seat speaks, and the seatback assistant answers them instead.
There is no record of who actually triggered the response. For compliance teams, that missing record is the real exposure.
The fix
One pre-ASR layer that decides who the device should answer, and logs it.
SAA is a hosted, pre-ASR layer: it decides, before speech-to-text, whether speech at a gate, check-in, or seatback is addressed to the device or to someone else nearby.
It routes only addressed audio to your existing ASR and voice model, fails closed when uncertain, and logs every decision as an attribution record.
Research foundation
Published and measured on multi-party audio, not a clean studio.
SAA reliably tells whether speech is addressed to the device, evaluated on held-out multi-party sessions (arXiv:2604.08412).
It fails closed under distribution shift, and cross-lingual recall is a known limitation under active work.
Audio-visual fusion is the higher-recall mode for cabin deployments with a per-seat camera; audio-only covers gate and check-in kiosks with no camera stream.
performance
Reliable on audio and video fusion
Held-out multi-party sessions. Published at arXiv:2604.08412.
performance
Reliable on audio-only
Real multi-speaker audio, no video stream. Published at arXiv:2604.08412.
latency
Real-time decision, before STT
The routing decision returns in real time, before ASR begins, with no GPU required and no perceptible delay.
design principle
Fails closed by design
Ambiguous speech is suppressed, not passed through. The default state under uncertainty is silence, not a false trigger.
signal model
Acoustic; no language parsing
No wake word. Language-agnostic at the acoustic level, relevant for multilingual gate and check-in environments.
How it integrates
Where SAA sits in your aviation voice stack.
SAA sits between your gate, kiosk, or seatback microphone and your ASR. Only audio addressed to the device continues downstream.
No wake word. No GPU. No change to your passenger interface or hardware.
Audio streams in; SAA classifies it before ASR runs
It reads acoustic patterns, not speech content, so the same model works across languages at the acoustic level.
Only addressed audio reaches your ASR
PA announcements, companion speech, crowd noise, and adjacent conversations are suppressed before transcription ever runs.
Every decision is logged: your attribution record
Every routing decision is logged per device with a timestamp: who was addressed, and when, at each interaction.
architecture
Pre-ASR, hosted, behind any voice model
Client SDKs in Python and C++ stream audio to server.attentionlabs.ai and return the decision before transcription begins.
SAA is model-agnostic: it works in front of any ASR and voice model you already run.
access and entry point
Convertible into pilot credit or a strategic option
After a successful go/no-go, the evaluation fee converts into pilot credit or a strategic investment option.
Book a callStructured evaluation
A 4 to 6 week paid evaluation with a written go/no-go.
The recommended entry environment is gate or check-in. You provide audio; we measure four agreed metrics against your baseline and deliver a written verdict.
Weeks 1 to 2
Integration and Baseline
You provide 10 to 20 hours of audio from the target environment, including PA announcements and ambient crowd. We deliver API keys, an SDK, and the eval kit.
Weeks 3 to 4
Evaluation Under Target Conditions
The SAA-gated pipeline runs against the same audio, measuring four agreed metrics against your baseline. Edge cases are logged separately.
Weeks 5 to 6
Written Go/No-Go Report
Per-metric results versus baseline, distribution shift notes, and a recommendation for the next environment.
False trigger rate: fraction of non-addressed audio (PA, crowd, companion) that reaches ASR
Addressed-speech F1: accuracy on audio genuinely directed at the device
Decision latency: time from audio frame ingress to routing verdict
PA noise tolerance: subset test with concurrent announcements at measured decibel levels
Why existing tools miss it
Beamforming and VAD do not decide who is addressing the kiosk.
Each layer below solves a real acoustic problem. None of them decides who the device is being addressed by.
That decision has to happen before ASR, or every false trigger reaches the part of your pipeline that acts on it.
| Layer | What it answers | When it runs | Decides who is addressed? |
|---|---|---|---|
| Noise cancellation and AEC | Is the audio cleaner? | Pre-ASR | No |
| Beamforming | Which direction is the sound coming from? | Pre-ASR | No |
| Voice activity detection (VAD) | Is someone speaking? | Pre-ASR | No |
| Speaker diarization | Who spoke, after the fact? | Post-ASR | No |
| Wake word | Was a trigger phrase said? | Pre-ASR | Phrase only |
| Speaker ID | Is this a known voice? | Post-ASR | No |
| SAA addressee detection | Is the device being addressed, right now? | Pre-ASR, in real time | Yes |
Noise cancellation and AEC
Beamforming
Voice activity detection (VAD)
Speaker diarization
Wake word
Speaker ID
SAA addressee detection
Deep dives: addressee detection vs VAD · addressee detection vs speaker diarization · addressee detection vs wake word
Voice AI that works at the gate and in the cabin: it knows who it's being addressed by before a word reaches ASR.
Common questions
What aviation and innovation lab teams ask before evaluating.
Why does airport voice AI fire on PA announcements?
Gate kiosk and check-in microphones capture every sound in range: the boarding PA calling groups overhead, a companion standing next to the transacting passenger, crowd chatter at 80 to 90 decibels, and adjacent kiosk sessions running concurrently. Without a layer that decides who is addressing the device, all of it reaches the ASR as potential user input. The PA announcement is not a recognizer failure; the system simply has no signal for which speaker was addressing the kiosk. SAA adds that decision before transcription runs.
Which environment should we start the evaluation in?
The recommended entry environment is gate or check-in. Both are bounded installations with measurable interaction volumes, no cabin certification burden, and a business unit (Operations or CX) that owns a clear KPI such as false trigger rate or passenger handle time. The cabin seatback environment is a valid second step after a gate or check-in evaluation closes successfully.
What does the 4-6 week evaluation look like?
Weeks 1 and 2 cover integration and baseline. You provide representative audio from the target environment, typically 10 to 20 hours of interactions including PA announcements, ambient crowd, and true addressed sessions. We deliver API keys, a client SDK in Python or C++, and the eval kit. Integration is a single pre-ASR stage in your existing pipeline. Weeks 3 and 4 run the SAA-gated pipeline against the same audio, measuring four agreed metrics: false trigger rate, addressed-speech F1, decision latency, and PA noise tolerance. Weeks 5 and 6 produce the written go/no-go report with per-metric results versus baseline, distribution shift observations, and a recommendation for the next environment. The evaluation fee is convertible into pilot credit or a strategic option.
Is audio sent to the cloud, and is a DPA available?
Yes. SAA is a hosted cloud service. Audio streams to the inference endpoint at server.attentionlabs.ai and the addressee decision returns in real time, before speech-to-text. SAA fails closed when confidence is below threshold, so unaddressed audio and uncertain audio are not forwarded downstream. A Data Processing Agreement is available for regulated environments and is standard for aviation deployments. Request one at the time of evaluation.
What metrics does the evaluation measure against our baseline?
The evaluation measures four metrics against your existing pipeline baseline: false trigger rate, the fraction of non-addressed audio events such as PA, ambient crowd, bystander, and companion that reach ASR; addressed-speech F1, the accuracy on audio genuinely directed at the device; decision latency, the time from audio frame ingress to routing verdict, which must return in real time, before speech-to-text; and PA noise tolerance, a specific subset test with concurrent PA announcements running at measured decibel levels representative of your environment. Low-confidence abstentions, the events where SAA fails closed, are logged separately.
How is SAA licensed after a successful evaluation?
SAA is licensed per device, not by usage or call volume. The evaluation is a paid, structured engagement; the fee is convertible into pilot credit or a strategic investment option after a successful go/no-go. Commercial licensing terms are discussed per deployment after the evaluation closes. Book a call to scope the evaluation and discuss terms.
Does SAA produce an attribution record of who was addressed?
Yes. Every routing decision is logged per device with a timestamp, producing a record of which speaker was classified as the principal speaker at each interaction point. For operations and compliance teams this is the attribution record a conventional voice stack does not produce: a structured log showing which interactions were addressed to the device and which were suppressed as PA, ambient, companion, or uncertain. The log is available for review during the evaluation and is part of the data delivered in the written go/no-go report.
Get in touch
Book a call or request an eval.
Tell us your target environment: gate, check-in, or cabin. We will scope the evaluation and agree the four metrics before week one.
Commercial licensing is per device and negotiated after the written go/no-go. A DPA is available and standard for aviation.
CES Picks 2026 Winner (TechRadar Pro / TWICE). Backed by a16z Scout. Published: arXiv:2604.08412.
Or try the Cloud SDK to get a feel.
Last updated June 27, 2026