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ARXIV:2604.08412 · ON-DEVICE VOICE AI · SUBMITTED 10 APR · 17:38 UTC · FRESHNESS STALE
ARXIV:2604.08412ON-DEVICE VOICE AISUBMITTED 10 APR · 17:38 UTCFRESHNESS STALEDavid Joohun Kim · Daniyal Anjum · Bonny Banerjee · Omar Abbasi · arXiv
A selective attention system for device-addressed speech detection that operates on-device with low latency and footprint, achieving high accuracy with optional video fusion.
Opportunity summary
Pain A selective attention system for device-addressed speech detection that operates on-device with low latency and footprint, achieving high accuracy with optional video fusion.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A selective attention system for device-addressed speech detection that operates on-device with low latency and footprint, achieving high accuracy with optional video fusion. We show that, in multi-speaker environments with temporally ambiguous utterances, this…
We study device-addressed speech detection under pre-ASR edge deployment constraints, where systems must decide whether to forward audio before transcription under strict latency and compute limits. We show that, in multi-speaker environments with temporally…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We show that, in multi-speaker environments with temporally ambiguous utterances, this task is more effectively modelled as a sequential routing problem over interaction history…
On-Device Voice AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
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A selective attention system for device-addressed speech detection that operates on-device with low latency and footprint, achieving high accuracy with optional video fusion.
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10.48550/arXiv.2604.08412A selective attention system for device-addressed speech detection that operates on-device with low latency and footprint, achieving high accuracy with optional video fusion.
Abstract
We study device-addressed speech detection under pre-ASR edge deployment constraints, where systems must decide whether to forward audio before transcription under strict latency and compute limits. We show that, in multi-speaker environments with temporally ambiguous utterances, this task is more effectively modelled as a sequential routing problem over interaction history than as an utterance-local classification task. We formalize this as Sequential Device-Addressed Routing (SDAR) and present the Selective Attention System (SAS), an on-device implementation that instantiates this formulation. On a held-out 60-hour multi-speaker English test set, the primary audio-only configuration achieves F1=0.86 (precision=0.89, recall=0.83); with an optional camera, audio+video fusion raises F1 to 0.95 (precision=0.97, recall=0.93). Removing causal interaction history (Stage~3) reduced F1 from 0.95 to 0.57+/-0.03 in the audio+video configuration under our evaluation protocol. Among the tested components, this was the largest observed ablation effect, indicating that short-horizon interaction history carries substantial decision-relevant information in the evaluated setting. SAS runs fully on-device on ARM Cortex-A class hardware (<150 ms latency, <20 MB footprint). All results are from internal evaluation on a proprietary dataset evaluated primarily in English; a 5-hour evaluation subset may be shared for independent verification (Section 8.8).
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PROBLEM
A selective attention system for device-addressed speech detection that operates on-device with low latency and footprint, achieving high accuracy with optional video fusion. We show that, in multi-speaker environments with temporally ambiguous utterances, this task is more effe...
METHOD
We study device-addressed speech detection under pre-ASR edge deployment constraints, where systems must decide whether to forward audio before transcription under strict latency and compute limits. We show that, in multi-speaker environments with temporally ambiguous utterances...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We show that, in multi-speaker environments with temporally ambiguous utterances, this task is more effectively modelled as a sequential routing problem over interaction history than as an utterance-local...
WHY NOW
On-Device Voice AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A selective attention system for device-addressed speech detection that operates on-device with low latency and footprint, achieving high accuracy with optional video fusion. We show that, in multi-speaker environments with temporally ambiguous utterances, this task is more effectively modelled as a sequential routing problem over interaction history than as an utterance-local classification task.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
We study device-addressed speech detection under pre-ASR edge deployment constraints, where systems must decide whether to forward audio before transcription under strict latency and compute limits. We show that, in multi-speaker environments with temporally ambiguous utterances, this task is more effectively modelled as a sequential routing problem over interaction history than as an utterance-local classification task.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We show that, in multi-speaker environments with temporally ambiguous utterances, this task is more effectively modelled as a sequential routing problem over interaction history than as an utterance-local classification task. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
On-Device Voice AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A selective attention system for device-addressed speech detection that operates on-device with low latency and footprint, achieving high accuracy with optional video fusion.
Segment
On-Device Voice AI
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