TY - GEN
T1 - Automatic recognition of physiological parameters in the human voice
T2 - 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
AU - Schuller, Björn
AU - Friedmann, Felix
AU - Eyben, Florian
PY - 2013/10/18
Y1 - 2013/10/18
N2 - 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.
AB - 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.
KW - Computational Paralinguistics
KW - Heart Rate
KW - Skin Conductance
KW - Speech Analysis
UR - http://www.scopus.com/inward/record.url?scp=84890458673&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2013.6639064
DO - 10.1109/ICASSP.2013.6639064
M3 - Conference contribution
AN - SCOPUS:84890458673
SN - 9781479903566
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 7219
EP - 7223
BT - 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Y2 - 26 May 2013 through 31 May 2013
ER -