  Excessive sleepiness in attention-critical contexts can lead to adverse
events, such as car crashes. Detecting and monitoring sleepiness can help
prevent these adverse events from happening. In this paper, we use the Voiceome
dataset to extract speech from 1,828 participants to develop a deep transfer
learning model using Hidden-Unit BERT (HuBERT) speech representations to detect
sleepiness from individuals. Speech is an under-utilized source of data in
sleep detection, but as speech collection is easy, cost-effective, and
non-invasive, it provides a promising resource for sleepiness detection. Two
complementary techniques were conducted in order to seek converging evidence
regarding the importance of individual speech tasks. Our first technique,
masking, evaluated task importance by combining all speech tasks, masking
selected responses in the speech, and observing systematic changes in model
accuracy. Our second technique, separate training, compared the accuracy of
multiple models, each of which used the same architecture, but was trained on a
different subset of speech tasks. Our evaluation shows that the best-performing
model utilizes the memory recall task and categorical naming task from the
Boston Naming Test, which achieved an accuracy of 80.07% (F1-score of 0.85) and
81.13% (F1-score of 0.89), respectively.
