Pith Number

pith:665UGLL6

pith:2026:665UGLL6UCRNVSE7CHGK4EEO3C

not attested not anchored not stored refs resolved

Speaker-Disentangled Remote Speech Detection of Asthma and COPD Exacerbations

Sami O. Simons, Visara Urovi, Yuyang Yan

Adversarial training separates speaker identity from disease signals in speech to improve detection of asthma and COPD exacerbations.

arxiv:2605.16878 v1 · 2026-05-16 · cs.SD

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\pithnumber{665UGLL6UCRNVSE7CHGK4EEO3C}

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

On the TACTICAS dataset, our method outperforms the single-task baseline across both tasks. For the respiratory status task (stable vs. exacerbated), the AUC improves from 0.897 to 0.910. For the exacerbation type task (asthma exacerbation vs. COPD exacerbation), the AUC increases from 0.674 to 0.793. Concurrently, the J-ratio decreases, confirming effective suppression of speaker information. External validation on the Bridge2AI-Voice dataset further demonstrates consistent performance improvement and reduced speaker dependency.

C2weakest assumption

The assumption that gradient reversal-based adversarial training successfully isolates pathology-related acoustic patterns from speaker-identifiable attributes without degrading the primary classification performance, as the abstract states this occurs but provides no quantitative verification of feature separation quality or ablation studies.

C3one line summary

An adversarial disentanglement framework improves speech-based detection of asthma and COPD exacerbations while suppressing speaker identity on the TACTICAS and Bridge2AI-Voice datasets.

References

44 extracted · 44 resolved · 0 Pith anchors

[1] Chronic obstructive pulmonary disease (copd), 2024

[2] A single home visit improves adherence and reduces healthcare utilization in patients with frequent exacerbations of severe asthma and copd, 2021

[3] Factors associated with frequency of emergency department visits for chronic obstructive pulmonary disease exacerbation, 2007

[4] A personalized respiratory disease exacerbation prediction technique based on a novel spatio-temporal machine learning architecture and local environmental sensor networks, 2022

[5] Concomitant diagnosis of asthma and copd: a quantitative study in uk primary care, 2018

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:03:27.833660Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

f7bb432d7ea0a2dac89f11ccae108ed89c286599148e49299e342fc3992a7839

Aliases

arxiv: 2605.16878 · arxiv_version: 2605.16878v1 · doi: 10.48550/arxiv.2605.16878 · pith_short_12: 665UGLL6UCRN · pith_short_16: 665UGLL6UCRNVSE7 · pith_short_8: 665UGLL6

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/665UGLL6UCRNVSE7CHGK4EEO3C \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: f7bb432d7ea0a2dac89f11ccae108ed89c286599148e49299e342fc3992a7839

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "21435a63ecb5a9bdf7a76be212a6d3f5501e5e52731d7d6366a0c24fe6f18ac3",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "cs.SD",
    "submitted_at": "2026-05-16T08:39:30Z",
    "title_canon_sha256": "736229467af5942990e41fb8a8ce553f9160250c483a6ea8aeb94615c2895b7a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16878",
    "kind": "arxiv",
    "version": 1
  }
}