DETECTION OF COPD EXACERBATION FROM SPEECH: COMPARISON OF ACOUSTIC FEATURES AND DEEP LEARNING BASED SPEECH BREATHING MODELS

Venkata Srikanth Nallanthighal; Aki Härmä; Helmer Strik

doi:10.1109/ICASSP43922.2022.9747785

DETECTION OF COPD EXACERBATION FROM SPEECH: COMPARISON OF ACOUSTIC FEATURES AND DEEP LEARNING BASED SPEECH BREATHING MODELS

Venkata Srikanth Nallanthighal^*, Aki Härmä, Helmer Strik

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

Abstract

Respiration is a primary process involved in speech production. We can often hear if a person has respiratory difficulty, thus making speech a good pathological indicator for respiratory conditions. This is more relevant to conditions like chronic obstructive pulmonary disease (COPD). Patients with COPD suffer from voice changes with respect to the healthy population. Medical professionals observe that the speech of COPD patients during stable periods differs from the speech during exacerbation. In this paper, we investigate this detection of COPD exacerbation from speech in three approaches: acoustic features identification using a statistical approach, low-level descriptive features with classification, and speech breathing models based on deep learning architectures to estimate the patients' breathing rate. Our analysis indicates that each of these approaches indeed results in a clear distinction of speech during exacerbation and stable periods of COPD.

Original language	English
Title of host publication	2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
Publisher	IEEE
Pages	9097-9101
Number of pages	5
ISBN (Electronic)	9781665405409
DOIs	https://doi.org/10.1109/ICASSP43922.2022.9747785
Publication status	Published - 2022
Externally published	Yes
Event	47th IEEE International Conference on Acoustics, Speech and Signal Processing - Online, Singapore, Singapore Duration: 22 May 2022 → 27 May 2022 Conference number: 47 https://2022.ieeeicassp.org/

Publication series

Series	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2022-May
ISSN	1520-6149

Conference

Conference	47th IEEE International Conference on Acoustics, Speech and Signal Processing
Abbreviated title	ICASSP 2022
Country/Territory	Singapore
City	Singapore
Period	22/05/22 → 27/05/22
Internet address	https://2022.ieeeicassp.org/

Keywords

COPD exacerbation
deep neural networks
speech breathing
speech technology
statistical approach

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10.1109/ICASSP43922.2022.9747785

Cite this

Nallanthighal, Venkata Srikanth ; Härmä, Aki ; Strik, Helmer. / DETECTION OF COPD EXACERBATION FROM SPEECH : COMPARISON OF ACOUSTIC FEATURES AND DEEP LEARNING BASED SPEECH BREATHING MODELS. 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. IEEE, 2022. pp. 9097-9101 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Vol. 2022-May).

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title = "DETECTION OF COPD EXACERBATION FROM SPEECH: COMPARISON OF ACOUSTIC FEATURES AND DEEP LEARNING BASED SPEECH BREATHING MODELS",

abstract = "Respiration is a primary process involved in speech production. We can often hear if a person has respiratory difficulty, thus making speech a good pathological indicator for respiratory conditions. This is more relevant to conditions like chronic obstructive pulmonary disease (COPD). Patients with COPD suffer from voice changes with respect to the healthy population. Medical professionals observe that the speech of COPD patients during stable periods differs from the speech during exacerbation. In this paper, we investigate this detection of COPD exacerbation from speech in three approaches: acoustic features identification using a statistical approach, low-level descriptive features with classification, and speech breathing models based on deep learning architectures to estimate the patients' breathing rate. Our analysis indicates that each of these approaches indeed results in a clear distinction of speech during exacerbation and stable periods of COPD.",

keywords = "COPD exacerbation, deep neural networks, speech breathing, speech technology, statistical approach",

author = "Nallanthighal, {Venkata Srikanth} and Aki H{\"a}rm{\"a} and Helmer Strik",

note = "Funding Information: This work was partially supported by the Horizon H2020 Marie Sk{\l}odowska-Curie Actions Initial Training Network European Training Network project under grant agreement No. 766287 (TAPAS) and Data Science Department, Philips Research, Eindhoven. Publisher Copyright: {\textcopyright} 2022 IEEE; 47th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022 ; Conference date: 22-05-2022 Through 27-05-2022",

year = "2022",

doi = "10.1109/ICASSP43922.2022.9747785",

language = "English",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

publisher = "IEEE",

pages = "9097--9101",

booktitle = "2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings",

address = "United States",

url = "https://2022.ieeeicassp.org/",

}

Nallanthighal, VS, Härmä, A & Strik, H 2022, DETECTION OF COPD EXACERBATION FROM SPEECH: COMPARISON OF ACOUSTIC FEATURES AND DEEP LEARNING BASED SPEECH BREATHING MODELS. in 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. IEEE, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2022-May, pp. 9097-9101, 47th IEEE International Conference on Acoustics, Speech and Signal Processing, Singapore, Singapore, 22/05/22. https://doi.org/10.1109/ICASSP43922.2022.9747785

DETECTION OF COPD EXACERBATION FROM SPEECH: COMPARISON OF ACOUSTIC FEATURES AND DEEP LEARNING BASED SPEECH BREATHING MODELS. / Nallanthighal, Venkata Srikanth; Härmä, Aki; Strik, Helmer.
2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. IEEE, 2022. p. 9097-9101 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Vol. 2022-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

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T2 - 47th IEEE International Conference on Acoustics, Speech and Signal Processing

AU - Nallanthighal, Venkata Srikanth

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AU - Strik, Helmer

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N2 - Respiration is a primary process involved in speech production. We can often hear if a person has respiratory difficulty, thus making speech a good pathological indicator for respiratory conditions. This is more relevant to conditions like chronic obstructive pulmonary disease (COPD). Patients with COPD suffer from voice changes with respect to the healthy population. Medical professionals observe that the speech of COPD patients during stable periods differs from the speech during exacerbation. In this paper, we investigate this detection of COPD exacerbation from speech in three approaches: acoustic features identification using a statistical approach, low-level descriptive features with classification, and speech breathing models based on deep learning architectures to estimate the patients' breathing rate. Our analysis indicates that each of these approaches indeed results in a clear distinction of speech during exacerbation and stable periods of COPD.

AB - Respiration is a primary process involved in speech production. We can often hear if a person has respiratory difficulty, thus making speech a good pathological indicator for respiratory conditions. This is more relevant to conditions like chronic obstructive pulmonary disease (COPD). Patients with COPD suffer from voice changes with respect to the healthy population. Medical professionals observe that the speech of COPD patients during stable periods differs from the speech during exacerbation. In this paper, we investigate this detection of COPD exacerbation from speech in three approaches: acoustic features identification using a statistical approach, low-level descriptive features with classification, and speech breathing models based on deep learning architectures to estimate the patients' breathing rate. Our analysis indicates that each of these approaches indeed results in a clear distinction of speech during exacerbation and stable periods of COPD.

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Nallanthighal VS, Härmä A, Strik H. DETECTION OF COPD EXACERBATION FROM SPEECH: COMPARISON OF ACOUSTIC FEATURES AND DEEP LEARNING BASED SPEECH BREATHING MODELS. In 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. IEEE. 2022. p. 9097-9101. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Vol. 2022-May). doi: 10.1109/ICASSP43922.2022.9747785

DETECTION OF COPD EXACERBATION FROM SPEECH: COMPARISON OF ACOUSTIC FEATURES AND DEEP LEARNING BASED SPEECH BREATHING MODELS

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Keywords

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