Sleep is a key point in our physical and mental well-being. To date, Polysomnography (PSG) is the gold standard in the analysis of sleep disorders. Sleep scoring procedure has always been affected by intra and inter-operator variability. Introducing AI into clinical practice could save physicians time and increase the agreement between different sleep centres. This Thesis aims to investigate alternative methods in the use of multi-scored datasets in Automatic Sleep Staging. The model used is an existing Deep Learning (DL) architecture: DeepSleepNet-Lite. Two experiments have been performed on two open-source multi-scored datasets. Experiment-1 introduces a new method called Empirical Label Smoothing.