TY - GEN
T1 - Sleepiness detection from speech by perceptual features
AU - Gunsel, Bilge
AU - Sezgin, Cenk
AU - Krajewski, Jarek
PY - 2013/10/18
Y1 - 2013/10/18
N2 - We propose a two-class classification scheme with a small number of features for sleepiness detection. Unlike the conventional methods that rely on the linguistics content of speech, we work with prosodic features extracted by psychoacoustic masking in spectral and temporal domain. Our features also model the variations between non-sleepy and sleepy modes in a quasi-continuum space with the help of code words learned by a bag-of-features scheme. These improve the unweighted recall rates for unseen people and minimize the language dependence. Recall rates reported based on Karolinska Sleepiness Scale (KSS) for Support Vector Machine and Learning Vector Quantization classifiers show that the developed system enable us monitoring sleepiness efficiently with a lower complexity compared to the reported benchmarking results for Sleepy Language Corpus.
AB - We propose a two-class classification scheme with a small number of features for sleepiness detection. Unlike the conventional methods that rely on the linguistics content of speech, we work with prosodic features extracted by psychoacoustic masking in spectral and temporal domain. Our features also model the variations between non-sleepy and sleepy modes in a quasi-continuum space with the help of code words learned by a bag-of-features scheme. These improve the unweighted recall rates for unseen people and minimize the language dependence. Recall rates reported based on Karolinska Sleepiness Scale (KSS) for Support Vector Machine and Learning Vector Quantization classifiers show that the developed system enable us monitoring sleepiness efficiently with a lower complexity compared to the reported benchmarking results for Sleepy Language Corpus.
KW - audio emotion detection
KW - human-machine interaction
KW - sleepiness detection
UR - http://www.scopus.com/inward/record.url?scp=84890529686&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2013.6637756
DO - 10.1109/ICASSP.2013.6637756
M3 - Conference contribution
AN - SCOPUS:84890529686
SN - 9781479903566
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 788
EP - 792
BT - 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
T2 - 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Y2 - 26 May 2013 through 31 May 2013
ER -