Detecting COPD Through Speech Analysis: A Dataset of Danish Speech and Machine Learning Approach
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arXiv
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| Format: | Preprint |
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2025
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| author | Sankey-Olsen, Cuno Olesen, Rasmus Hvass Eberhard, Tobias Oliver Triantafyllopoulos, Andreas Schuller, Björn Aslan, Ilhan |
| author_facet | Sankey-Olsen, Cuno Olesen, Rasmus Hvass Eberhard, Tobias Oliver Triantafyllopoulos, Andreas Schuller, Björn Aslan, Ilhan |
| contents | Chronic Obstructive Pulmonary Disease (COPD) is a serious and debilitating disease affecting millions around the world. Its early detection using non-invasive means could enable preventive interventions that improve quality of life and patient outcomes, with speech recently shown to be a valuable biomarker. Yet, its validity across different linguistic groups remains to be seen. To that end, audio data were collected from 96 Danish participants conducting three speech tasks (reading, coughing, sustained vowels). Half of the participants were diagnosed with different levels of COPD and the other half formed a healthy control group. Subsequently, we investigated different baseline models using openSMILE features and learnt x-vector embeddings. We obtained a best accuracy of 67% using openSMILE features and logistic regression. Our findings support the potential of speech-based analysis as a non-invasive, remote, and scalable screening tool as part of future COPD healthcare solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_02354 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Detecting COPD Through Speech Analysis: A Dataset of Danish Speech and Machine Learning Approach Sankey-Olsen, Cuno Olesen, Rasmus Hvass Eberhard, Tobias Oliver Triantafyllopoulos, Andreas Schuller, Björn Aslan, Ilhan Sound Human-Computer Interaction Machine Learning Audio and Speech Processing Chronic Obstructive Pulmonary Disease (COPD) is a serious and debilitating disease affecting millions around the world. Its early detection using non-invasive means could enable preventive interventions that improve quality of life and patient outcomes, with speech recently shown to be a valuable biomarker. Yet, its validity across different linguistic groups remains to be seen. To that end, audio data were collected from 96 Danish participants conducting three speech tasks (reading, coughing, sustained vowels). Half of the participants were diagnosed with different levels of COPD and the other half formed a healthy control group. Subsequently, we investigated different baseline models using openSMILE features and learnt x-vector embeddings. We obtained a best accuracy of 67% using openSMILE features and logistic regression. Our findings support the potential of speech-based analysis as a non-invasive, remote, and scalable screening tool as part of future COPD healthcare solutions. |
| title | Detecting COPD Through Speech Analysis: A Dataset of Danish Speech and Machine Learning Approach |
| topic | Sound Human-Computer Interaction Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2508.02354 |