Detection of mild dyspnea from pairs of speech recordings

Sander Boelders, Venkata Srikanth Nallanthighal, Vlado Menkovski, Aki Härmä

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

5 Citations (Scopus)

Abstract

Shortness of breath, or dyspnea is a condition of the cardiopulmonary system that may be caused by, for example, a heart or lung disease, or physical load. In this paper, we explore techniques of detecting mild dyspnea directly from conversational speech, for example, in a telehealth application. We demonstrate with a collection of speech recordings before and after a light physical exercise that a siamese neural network, when presented examples of the two conditions, can detect the difference between two speech signals. This shows that this signal can be detected using data-pairs, removing the need for ratings of severity or the distinction of separate classes.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers
Pages4102-4106
Number of pages5
ISBN (Electronic)9781509066315
DOIs
Publication statusPublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2020) - Virtual, Barcelona, Spain
Duration: 4 May 20208 May 2020
https://2020.ieeeicassp.org/

Conference

Conference2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2020)
Abbreviated titleICASSP 2020
Country/TerritorySpain
CityBarcelona
Period4/05/208/05/20
Internet address

Keywords

  • Data pairs
  • Deeplearning
  • Dyspnea
  • Health monitoring
  • Speech signal processing

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