This paper is about automatic sleepiness state detection using speech samples. Following previous research carried out for the Interspeech 2011 challenge, we use the Sleepy Language Corpus (SLC) for our experiments. However, as we are willing to record our own subjects within a collaboration project with the Bordeaux hospital, we focus only on the read speech samples of that database. Furthermore, we are looking for understandable cues that can guide clinicians to provide a diagnostic. Hence, we devised a set of meaningful features that are close to the signal and restrict the feature selection process to methods that do not use feature combinations. Thus, using simple correlations and a grid search procedure on the training and development parts of the database, we selected a final set of 23 features, reaching a performance on par with state-of-the-art systems. A discussion is proposed on the subjective ground truth used for the boundary between sleepy and non sleepy speech in this database. Finally, we discuss on the interpretation of the features and provide hints on the physiological causes.