1. Elsner, D., Langer, S., Ritz, F., Mueller, R., and Illium, S. 2019. Deep neural baselines for computational paralinguistics. arXiv preprint arXiv:1907.02864.

Self-Replication Robustness The study presents an innovative end-to-end deep learning method to identify sleepiness in spoken language, as part of the Interspeech 2019 ComParE challenge. This method utilizes a deep neural network architecture to analyze audio data directly, eliminating the need for specific feature engineering. This approach not only achieves performance comparable to state-of-the-art models but is also adaptable to various audio classification tasks. For more details, refer to the work by [Elsner et al. 2019].