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Detecting Sleepiness by Fusing Classifiers Trained with Novel Acoustic Features

Automatic sleepiness detection is a challenging task that can lead to advances in various domains including traffic safety, medicine and human-machine interaction. This paper analyzes the discriminative power of different acoustic features to detect sleepiness. The study uses the sleepy language corpus (SLC). Along with standard acoustic features, novel features are proposed including functionals across voiced segment statistics in the F0 contour, likelihoods of reference models used to contrast non-neutral speech, and a set of robust to noise spectral features. These feature sets, which have performed well in other paralinguistic tasks such as emotion recognition, are used to train classifiers that are combined at the feature and decision levels. The best unweighted accuracy (UA) is obtained by combining the classifiers at the decision level under a maximum likelihood framework (UA = 70.97%). This performance is higher than the best results reported in the corpus. Index Terms: Speaker State Recognition, Paralinguistics, Affective Computing, Sleepiness