Parkinson's disease is a neurodegenerative disorder characterized by a variety of motor symptoms. Particularly, difficulties to start/stop movements have been observed in patients. From a technical/diagnostic point of view, these movement changes can be assessed by modeling the transitions between voiced and unvoiced segments in speech, the movement when the patient starts or stops a new stroke in handwriting, or the movement when the patient starts or stops the walking process. This study proposes a methodology to model such difficulties to start or to stop movements considering information from speech, handwriting, and gait. We used those transitions to train convolutional neural networks to classify patients and healthy subjects. The neurological state of the patients was also evaluated according to different stages of the disease (initial, intermediate, and advanced). In addition, we evaluated the robustness of the proposed approach when considering speech signals in three different languages: Spanish, German, and Czech. According to the results, the fusion of information from the three modalities is highly accurate to classify patients and healthy subjects, and it shows to be suitable to assess the neurological state of the patients in several stages of the disease. We also aimed to interpret the feature maps obtained from the deep learning architectures with respect to the presence or absence of the disease and the neurological state of the patients. As far as we know, this is one of the first works that considers multimodal information to assess Parkinson's disease following a deep learning approach.

Background Dysarthric symptoms in Parkinson's disease (PD) vary greatly across cohorts. Abundant research suggests that such heterogeneity could reflect subject‐level and task‐related cognitive factors. However, the interplay of these variables during motor speech remains underexplored, let alone by administering validated materials to carefully matched samples with varying cognitive profiles and combining automated tools with machine learning methods. Objective We aimed to identify which speech dimensions best identify patients with PD in cognitively heterogeneous, cognitively preserved, and cognitively impaired groups through tasks with low (reading) and high (retelling) processing demands. Methods We used support vector machines to analyze prosodic, articulatory, and phonemic identifiability features. Patient groups were compared with healthy control subjects and against each other in both tasks, using each measure separately and in combination. Results Relative to control subjects, patients in cognitively heterogeneous and cognitively preserved groups were best discriminated by combined dysarthric signs during reading (accuracy = 84% and 80.2%). Conversely, patients with cognitive impairment were maximally discriminated from control subjects when considering phonemic identifiability during retelling (accuracy = 86.9%). This same pattern maximally distinguished between cognitively spared and impaired patients (accuracy = 72.1%). Also, cognitive (executive) symptom severity was predicted by prosody in cognitively preserved patients and by phonemic identifiability in cognitively heterogeneous and impaired groups. No measure predicted overall motor dysfunction in any group. Conclusions Predominant dysarthric symptoms appear to be best captured through undemanding tasks in cognitively heterogeneous and preserved cohorts and through cognitively loaded tasks in patients with cognitive impairment. Further applications of this framework could enhance dysarthria assessments in PD. © 2021 International Parkinson and Movement Disorder Society

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Aim: This paper introduces Apkinson, a mobile application for motor evaluation and monitoring of Parkinson’s disease patients. Materials & methods: The App is based on previously reported methods, for instance, the evaluation of articulation and pronunciation in speech, regularity and freezing of gait in walking, and tapping accuracy in hand movement. Results: Preliminary experiments indicate that most of the measurements are suitable to discriminate patients and controls. Significance is evaluated through statistical tests. Conclusion: Although the reported results correspond to preliminary experiments, we think that Apkinson is a very useful App that can help patients, caregivers and clinicians, in performing a more accurate monitoring of the disease progression. Additionally, the mobile App can be a personal health assistant.

Parkinson's disease (PD) produces several speech impairments in the patients. Automatic classification of PD patients is performed considering speech recordings collected in noncontrolled acoustic conditions during normal phone calls in a unobtrusive way. A speech enhancement algorithm is applied to improve the quality of the signals. Two different classification approaches are considered: the classification of PD patients and healthy speakers and a multi-class experiment to classify patients in several stages of the disease. According to the results it is possible to classify PD patients and healthy controls with a AUC of up to 0.87. This work is a step forward to the development of telemonitoring systems to assess the speech of the patients.

In this paper we want to present our work on a smartphone application which aims to provide a mobile monitoring solution for patients suffering from Parkinson's disease. By unobtrusively analyzing the speech signal during phone calls and with a dedicated speech test, we want to be able to determine the severity and the progression of Parkinson's disease for a patient much more frequently than it would be possible with regular checkups. The application consists of four major parts. There is a phone call detection which triggers the whole processing chain. Secondly, there is the phone call recording which has proven to be more challenging than expected. The signal analysis, another crucial component, is still in development for the phone call analysis. Additionally, the application collects several pieces of meta information about the calls to put the results into deeper context. After describing how the speech signal is affected by Parkinson's disease, we sketch the overall application architecture and explain the four major parts of the current implementation in further detail. We then present the promising results achieved with the first version of a dedicated speech test. In the end, we outline how the project could receive further improvements in the future.

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...