Language-Independent Sleepy Speech Detection.

Prolonged sleepiness can lead to impairment of cognitive and physical performance and may cause unfortunate accidents. Speech signals are easily accessible using a simple microphone or other means, hence, automated approaches for accurate sleepiness detection from speech signals are desired to prevent degradation in human performance and accidental injury. Sleepiness is known to affect acoustic patterns of speech so that they are different from those of normal speech, and this change is also independent of the language being spoken. To date, there have been no studies examining linguistic-independent sleepy speech detection. We used two different languages, English and German, to detect sleepy speech, where the former was used to train/validate and the latter to test the effectiveness of machine and deep learning models. Specifically, we trained ResNet50, a deep learning model, and five machine learning models with relevant vocal features. Speech data segments from three English-speaking subjects were used for training the model and segments from an English-speaking subject were used for validation. We then tested ResNet50 and the five different machine-learning models using speech data segments from one German-speaking subject. Deep learning far outperformed all of the machine learning approaches. The accuracy, sensitivity, specificity, and geometric mean values were found to be 0.96, 0.92, 0.99, and 0.95, respectively, using ResNet50 on the test data. Our preliminary results suggest that sleepiness can be accurately detected independently from linguistic speech. Clinical Relevance-It is not known if sleepiness can be detected regardless of the language spoken. Our results show the feasibility of accurate sleepiness detection using deep learning even when tested with a different language than trained on.