Applying multiple classifiers and non-linear dynamics features for detecting sleepiness from speech

Comparing different novel feature sets and classifiers for speech processing based fatigue detection is the primary aim of this study. Thus, we conducted a within-subject partial sleep deprivation design (20.00-04.00 h, N=77 participants) and recorded 372 speech samples of sustained vowel phonation. The self-report on the Karolinska Sleepiness Scale (KSS) and an observer report on the KSS, the KSS Observer Scale were applied to determine sleepiness reference values. Feature extraction methods of non-linear dynamics (NLD) provide additional information regarding the dynamics and structure of sleepiness speech. In all, 395 NLD features and the 170 phonetic features, which have been computed partially, represent so far unknown auditive-perceptual concepts. Several NLD and phonetic features show significant correlations to KSS ratings, e.g., from the NLD features for male speakers the skewness of vector length within reconstructed phase space (r=.56), and for female speaker the mean of Cao's minimum embedding dimensions (r=−.39). After a correlation-filter feature subset selection different classification models and ensemble classifiers (by AdaBoost, Bagging) were trained. Bagging procedures turned out to achieve best performance for male and female speakers on the phonetic and the NLD feature set. The best models for the phonetic feature set achieved 78.3% (NaïveBayes) for male and 68.5% (Bagging Bayes Net) for female speaker classification accuracy in detecting sleepiness. The best model for the NLD feature set achieved 77.2% (Bagging Bayes Net) for male and 76.8% (Bagging Bayes Net) for female speakers. Nevertheless, employing the combined phonetic and NLD feature sets provided additional information and thus resulted in an improved highest UA of 79.6% for male (Bayes Net) and 77.1% for female (AdaBoost Nearest Neighbor) speakers.