Speaker models for monitoring Parkinson’s disease progression considering different communication channels and acoustic conditions

•The paper introduces the use of speaker models (GMM-UBM and i-vectors) to evaluate the progression of Parkinson’s disease (PD) from speech. This is one of the first papers addressing the task of individual speaker models to assess Parkinson’s disease progression based on speech recordings captured in different recording sessions.•The suitability of the proposed approach for monitoring Parkinson’s patients from speech is evaluated considering recordings captured through different communication channels: Skype, Google Hangouts, landlines, and mobile phones.•Two different scenarios are considered to test the proposed approach: (i) longitudinal recordings captured from 2012 and 2016, and (ii) recordings captured in the home of the patients during 4 months (one day per month, every two hours and during 8 h).•The use of the two recording sets mentioned above make the experiments reported in this paper highly original and novel, thus we consider that this work is a significant contribution to the development of computer-aided tools to monitor people suffering from Parkinson’s disease. Symptoms of Parkinson’s disease vary from patient to patient. Additionally, the progression of those symptoms also differs among patients. Most of the studies on the analysis of speech of people with Parkinson’s disease do not consider such an individual variation. This paper presents a methodology for the automatic and individual monitoring of speech disorders developed by PD patients. The neurological state and dysarthria level of the patients are evaluated. The proposed system is based on individual speaker models which are created for each patient. Two different models are evaluated, the classical GMM–UBM and the i–vectors approach. These two methods are compared with respect to a baseline found with a traditional Support Vector Regressor. Different speech aspects (phonation, articulation, and prosody) are considered to model recordings of spontaneous speech and a read text. A multi-aspect coefficient is proposed with the aim of incorporating information from all of these speech aspects into a single measure. Two different scenarios are considered to assess a set with seven PD patients: (1) the longitudinal test set which consists of speech recordings captured in five recording sessions distributed from 2012 to 2016, and (2) the at-home test set which consists of speech recordings captured in the home of the same seven patients during 4 months (one day per month, four times per