# Speech-Based Support System to Supervise Chronic Obstructive Pulmonary Disease Patient Status
## 5 Conclusions  
The presented work explores the effect of physical exertion and medication on speech features in subjects with COPD. The effects of physical effort have been compared to those produced by a control group. The results have shown that several speech features, ranging from f0, voice quality, to prosodic features based on intonation and rhythm, might suffer signification changes under both physical effort and inhaled medication, and that the differences of the physical effort and medication effects are consistent in intonation and rhythm prosodic features. While effort causes a more flattened intonation and lower rhythm characteristics, medication seems to help patients to achieve more expressive speech and a higher rhythm, reflected as higher speech rates, a smaller number of pauses, and lower segment durations.  
The current study also explored the performance of a classification system to discern between COPD/non-COPD subjects, and to predict FEV1. Very few studies deal with classification of COPD variables. The results achieved up to 72% of classification accuracy over a baseline of 50%, which are comparable to--and even outperform--the ones obtained in previous studies such as [19], where COPD and non-COPD individuals were found differentiable with an accuracy of 68%. Moreover, it was shown that beyond the specific accuracy values obtained, modifications in speech feature values due to different conditions can affect the performance of a COPD automatic detection system, so that its design regarding the recording conditions is important for a successful performance.  
This work represents a proof of concept for COPD patient supervision using machine learning algorithms and takes a step forward to the understanding of COPD, and to the benefits of automatic detection of COPD variables from speech, which could be useful for clinicians working in the respiratory field.  
**Author Contributions:** [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] conceived and designed the study. [PERSON] and [PERSON] monitored the planning and execution of the study. [PERSON], [PERSON] and [PERSON] obtained the ethical approval for the study. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] participated in the data collection. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] participated in the data analysis and explanation. [PERSON], [PERSON] and [PERSON] wrote the first draft, and [PERSON] and [PERSON] helped also with the critical revision. All authors contributed to the final version, and all of them have read and agreed to the published version of the manuscript.  
**Funding:** This research has been funded by GlaxoSmithKline, S.A. The first author has been funded by the Agencia Estatal de Investigacion (AEI), Ministerio de Ciencia, Innovacion y Universidades and the Fondo Social Europeo (FSE) under grant RYC-2015-17239 (AEI/FSE, UE).  
**Institutional Review Board Statement:** The ethics committee of the Hospital Clinic in Barcelona approved the study design and protocol (HCB/2018/1190).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy and ethical issues.  
**Acknowledgments:** The authors would like to thank all the participants that took part in this study and the following professionals from CAPSBE: [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.