This article describes a general framework for detecting sleepiness states on the basis of prosody, articulation, and speech-quality-related speech characteristics. The advantages of this automatic real-time approach are that obtaining speech data is nonobstrusive and is free from sensor application and calibration efforts. Different types of acoustic features derived from speech, speaker, and emotion recognition were employed (frame-level-based speech features). Combing these features with high-level contour descriptors, which capture the temporal information of frame-level descriptor contours, results in 45,088 features per speech sample. In general, the measurement proceß follows the speech-adapted steps of pattern recognition: (1) recording speech, (2) preproceßing, (3) feature computation (using perceptual and signal-proceßing-related features such as, e.g., fundamental frequency, intensity, pause patterns, formants, and cepstral coefficients), (4) dimensionality reduction, (5) claßification, and (6) evaluation. After a correlation-filter-based feature subset selection employed on the feature space in order to find most relevant features, different claßification models were trained. The best model—namely, the support-vector machine—achieved 86.1% claßification accuracy in predicting sleepineß in a sleep deprivation study (two-claß problem, N 5 12; 01.00-08.00 a.m.).