Cross-Domain Classification of Drowsiness in Speech: The Case of Alcohol Intoxication and Sleep Deprivation

In this work, we study the drowsy state of a speaker, induced by alcohol intoxication or sleep deprivation. In particular, we investigate the coherence between the two pivotal causes of drowsiness, as featured in the Intoxication and Sleepiness tasks of the INTERSPEECH Speaker State Challenge. In this way, we aim to exploit the interrelations between these different, yet highly correlated speaker states, which need to be reliably recognised in safety and security critical environments. To this end, we perform cross-domain classification of alcohol intoxication and sleepiness, thus leveraging the acoustic similarities of these speech phenomena for transfer learning. Further, we conducted in-depth feature analysis to quantitatively assess the task relatedness and to determine the most relevant features for both tasks. To test our methods in realistic contexts, we use the Alcohol Language Corpus and the Sleepy Language Corpus containing in total 60 hours of genuine intoxicated and sleepy speech. In the result, cross-domain classification combined with feature selection yields up to 60.3 % unweighted average recall, which is significantly above-chance (50 %) and highly notable given the mismatch in the training and validation data. Finally, we show that an effective, general drowsiness classifier can be obtained by aggregating the training data from both domains.