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

Abstract

The interest of the research community in the analysis of speech of people suffering from Parkinson's disease has increased in recent years. Most of the studies are focused on developing computer-aided tools for the detection and unobtrusive monitoring the progression of several symptoms of the disease. Different approaches have been proposed to detect several voice impairments in PD patients. Most of the state-of-the-art studies address the task of assessing the neurological state of patients considering information obtained from a set of PD speakers, i.e., most of the reported studies consider regression techniques which are trained with information obtained from a group of patients and assess the neurological state of another group of patients suffering from PD. Such an approach seems to be a good alternative to evaluate the suitability of the models/measures extracted from the speech signals; however, that approach is not appropriate to perform individual monitoring of patients and including information about the progression of the disease in a specific person. Additionally, due to the difficulty of having continue access to PD patients, the number of contributions focused on the automatic monitoring of the patients is reduced. Most of the reported works are based on recordings captured during clinical appointments, i.e., relatively controlled acoustic and recording conditions. In this study we propose a methodology to assess the disease progression considering individual information per patient, i.e., individual speaker models. Two different methods are explored, one is based on the GMM-UBM approach and the other one is based on i-vectors. Both approaches have been successfully applied in speaker identification and verification tasks. In this paper the main hypothesis is that once the speech of a patient is accurately modeled, any change, like those that appear due to the disease progression, will be detected. Speech signals are modeled considering three speech aspects: phonation, articulation, and prosody. The results obtained with the proposed approaches are compared with respect to the traditional framework which is based on regression analysis. The models are trained considering a set with 100 speakers (50 suffering from PD and 50 healthy speakers). The tests are performed considering two sets with speech recordings 1 captured in real-world acoustic conditions. The first set contains a group of seven speakers recorded several times from 2012 to 2016, i.e., longitudinal recordings. As the acoustic conditions of those recordings were different between sessions, this corpus represents a realworld scenario to study the neurological state of PD patients. The second set is form with recordings of the same group of seven patients recorded in their houses, i.e., at-home recordings, those patients were recorded in 16 sessions during four months, i.e., one day per month, every two hours during eight hours per day. As in the case of the longitudinal recordings, the acoustic conditions were not controlled, thus this...

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