In the context of neurodegenerative diseases, vocal analysis is an easy and inexpensive way to extract useful elements for follow-up, prognostic prediction and rehabilitation of patients. Datasets with recordings of patients and healthy controls reading prompts have been collected to allow the development and evaluation of automatic vocal analysis systems. This work deals with the tasks of speech recognition and forced alignment, important building blocks for such systems, specifically on speech from Parkinson's Disease patients and healthy controls. A system has been designed and developed to perform automatic speech recognition and forced alignment, based on fine tuned state-of-the-art models, it can leverage unlabeled data and it can be used to align spontaneous speech with no prompts.