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 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.

Four sound sources converge on a gate kiosk microphone with no addressee gate The boarding PA, a companion, crowd noise, and the transacting passenger all reach the gate kiosk microphone. Without an addressee gate the voice AI acts on all of them, producing false triggers and missed passenger intent. Boarding PA Companion Crowd noise Passenger Gate kiosk mic Voice AI, no addressee gate Acts on all four sources False triggers on PA and crowd. Passenger intent is buried.
Lime marks the transacting passenger; amber dashes show the boarding PA as the dominant false trigger. Without an addressee gate, the voice AI cannot distinguish the passenger's intent from the surrounding acoustic environment.

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.

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.

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.

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.

How SAA routes only addressed audio in an aviation environment Raw aviation mic audio from gate, check-in, or seatback streams into the hosted SAA service. SAA returns an addressee decision in real time, before ASR. Addressed audio is routed to the existing ASR and voice model. Audio that is not addressed or is uncertain is not forwarded; the system fails closed. Aviation mic gate / check-in / seatback all voices in range SAA addressee decision real time, pre-ASR Addressed to ASR and voice model Not addressed suppressed; fails closed
SAA sits before ASR. Addressed speech is forwarded to the existing stack; PA announcements, companion speech, crowd noise, and anything uncertain stop at the gate.
01

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.

02

Only addressed audio reaches your ASR

PA announcements, companion speech, crowd noise, and adjacent conversations are suppressed before transcription ever runs.

03

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 call

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.

Metric 01

False trigger rate: fraction of non-addressed audio (PA, crowd, companion) that reaches ASR

Metric 02

Addressed-speech F1: accuracy on audio genuinely directed at the device

Metric 03

Decision latency: time from audio frame ingress to routing verdict

Metric 04

PA noise tolerance: subset test with concurrent announcements at measured decibel levels

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

What it answersIs the audio cleaner?
When it runsPre-ASR
Decides who is addressed?No

Beamforming

What it answersWhich direction is the sound coming from?
When it runsPre-ASR
Decides who is addressed?No

Voice activity detection (VAD)

What it answersIs someone speaking?
When it runsPre-ASR
Decides who is addressed?No

Speaker diarization

What it answersWho spoke, after the fact?
When it runsPost-ASR
Decides who is addressed?No

Wake word

What it answersWas a trigger phrase said?
When it runsPre-ASR
Decides who is addressed?Phrase only

Speaker ID

What it answersIs this a known voice?
When it runsPost-ASR
Decides who is addressed?No

SAA addressee detection

What it answersIs the device being addressed, right now?
When it runsPre-ASR, in real time
Decides who is addressed?Yes

Deep dives: addressee detection vs VAD · addressee detection vs speaker diarization · addressee detection vs wake word

Bottom line

Voice AI that works at the gate and in the cabin: it knows who it's being addressed by before a word reaches ASR.

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.

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.

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