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Communication

Speech-Based Support System to Supervise Chronic Obstructive Pulmonary Disease Patient Status

1
Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018 Barcelona, Spain
2
Language and Computation Centre, Universitat de Barcelona, 08007 Barcelona, Spain
3
Fundació TIC Salut Social, Departament de Salut, Generalitat de Catalunya, 08005 Barcelona, Spain
4
CAP Comte Borrell, Consorci d’Atenció Primària de Salut de Barcelona Esquerra (CAPSBE), 08029 Barcelona, Spain
5
Grup de Recerca Transversal en Atenció Primària, IDIBAPS, 08036 Barcelona, Spain
6
Universitat de Vic—Universitat Central de Catalunya, 08500 Vic, Spain
7
Department of Medicine, Universitat de Barcelona, 08036 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Michael Döllinger
Appl. Sci. 2021, 11(17), 7999; https://doi.org/10.3390/app11177999
Received: 6 July 2021 / Revised: 22 August 2021 / Accepted: 24 August 2021 / Published: 29 August 2021
(This article belongs to the Special Issue Applications of Speech and Language Technologies in Healthcare)
Patients with chronic obstructive pulmonary disease (COPD) suffer from voice changes with respect to the healthy population. However, two issues remain to be studied: how long-term speech elements such as prosody are affected; and whether physical effort and medication also affect the speech of patients with COPD, and if so, how an automatic speech-based detection system of COPD measurements can be influenced by these changes. The aim of the current study is to address both issues. To this end, long read speech from COPD and control groups was recorded, and the following experiments were performed: (a) a statistical analysis over the study and control groups to analyse the effects of physical effort and medication on speech; and (b) an automatic classification experiment to analyse how different recording conditions can affect the performance of a COPD detection system. The results obtained show that speech—especially prosodic features—is affected by physical effort and inhaled medication in both groups, though in opposite ways; and that the recording condition has a relevant role when designing an automatic COPD detection system. The current work takes a step forward in the understanding of speech in patients with COPD, and in turn, in the research on its automatic detection to help professionals supervising patient status. View Full-Text
Keywords: chronic obstructive pulmonary disease; COPD; machine learning; prosody; speech analysis chronic obstructive pulmonary disease; COPD; machine learning; prosody; speech analysis
MDPI and ACS Style

Farrús, M.; Codina-Filbà, J.; Reixach, E.; Andrés, E.; Sans, M.; Garcia, N.; Vilaseca, J. Speech-Based Support System to Supervise Chronic Obstructive Pulmonary Disease Patient Status. Appl. Sci. 2021, 11, 7999. https://doi.org/10.3390/app11177999

AMA Style

Farrús M, Codina-Filbà J, Reixach E, Andrés E, Sans M, Garcia N, Vilaseca J. Speech-Based Support System to Supervise Chronic Obstructive Pulmonary Disease Patient Status. Applied Sciences. 2021; 11(17):7999. https://doi.org/10.3390/app11177999

Chicago/Turabian Style

Farrús, Mireia, Joan Codina-Filbà, Elisenda Reixach, Erik Andrés, Mireia Sans, Noemí Garcia, and Josep Vilaseca. 2021. "Speech-Based Support System to Supervise Chronic Obstructive Pulmonary Disease Patient Status" Applied Sciences 11, no. 17: 7999. https://doi.org/10.3390/app11177999

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