Alcohol is a progressive central nervous system depressant. Increased alcohol consumption leads to alterations in cognitive processes and also affects speech production. In this study we present a corpus of n=35 patients diagnosed with Alcohol Dependency Syndrome (ADS) and n=35 matched healthy controls, and attempt to automatically distinguish the two speaker groups based on their spontaneous speech. By using wav2vec 2.0 embeddings as features, we were able to identify the two speaker categories with quite high accuracy (EER scores between 9% and 20%, and AUC scores above 0.885). We also sought to find the difference between the two speech tasks (a general spontaneous task and an alcohol-related one) performed by the subjects. Lastly, we analyzed the amount of pauses present in the speech of the subjects. Based on our results, even three simple pause-related attributes are sufficient for the automatic identification of the ADS subjects with an acceptable performance for both speech tasks.