Automatic recognition of physiological parameters in the human voice: Heart rate and skin conductance

Björn Schuller, Felix Friedmann, Florian Eyben

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

34 Scopus citations

Abstract

We show that high pulse/low pulse, heart rate and skin conductance recognition can reach good accuracies using classification on a large group of 4k audio features extracted from sustained vowels and breathing periods. A database containing audio, heart rate and skin conductance recordings from 19 subjects is established for evaluation of audio-based bio-signal recognition. On this database in speaker-dependent testing, heart rate and skin conductance can be determined with a correlation coefficient of.861/.960 and mean absolute error of 8.1 BPM/88.2 μMhO for regression based on sustained vowels recorded from a room microphone. Using the same set-up, a high pulse/low pulse classification can reach an unweighted accuracy of 82.7%. The results are largely independent from microphone type and the two bio-signals can be determined from breathing periods as well. Performance does, however, degrade in speaker-independent setting.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages7219-7223
Number of pages5
DOIs
StatePublished - 18 Oct 2013
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: 26 May 201331 May 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Country/TerritoryCanada
CityVancouver, BC
Period26/05/1331/05/13

Keywords

  • Computational Paralinguistics
  • Heart Rate
  • Skin Conductance
  • Speech Analysis

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