Zero-Shot Multi-Task Cough Sound Analysis with Speech Foundation Model Embeddings
Bibliographic record
Abstract
Supportive smart home systems with integrated personalized cough analysis can support independent living and aging in place by helping monitor the state of acute and chronic health conditions. The stages of recognizing coughs, associating them to an individual, and analyzing the cough characteristics have traditionally been handled independently, using task-specific networks or algorithms. In contrast, recent transformer-based neural network speech foundation models trained on internet-scale datasets have demonstrated strong performance across a wide range of tasks. Learning such a general-purpose cough representation has been hampered by the lack of large-scale cough-specific datasets. In this work we demonstrate that the embeddings from a speech foundation model (w2v BERT 2.0) can be used as a powerful multi-purpose cough representation. We show that cough information is well encoded in the model, despite it being trained on speech data with no cough-specific fine-tuning or adapters. Zero-shot linear classification on the cough embeddings achieves strong performance on cough/breathing/speech discrimination (100%), cougher verification (96.9%), cougher identification (84.4%), and wet/dry cough classification (93.8%) tasks. We also show that distance metrics between cough embeddings is meaningful and use that to conduct explainable analysis of an unlabelled sample with similarity-based retrieval from a labelled dataset. We note these capabilities emerge in the early layers of the network, and that the cough embeddings occupy a small region of the embedding space, motivating future work into lower-complexity cough-specific representations suitable for embedded cough analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".