Fairness-Aware Partial-label Domain Adaptation for Voice Classification of Parkinson's and ALS
organization= School of Computer；Communication Sciences, EPFL (\'Ecole polytechnique f\'ed\'erale de Lausanne) , city= Lausanne , country= Switzerland；organization= Eustema S.p.A., Research；Development Centre , city= Naples , country= Italy；organization= UniCamillus-Saint Camillus International University of Health Sciences , city= Rome , country= Italy；organization= Department of Diagnostics；Intervention, Radiation Physics, Biomedical Engineering, Umeå University , city= Umeå , country= Sweden
Arianna Francesconi, Zhixiang Dai, Arthur Stefano Moscheni, Himesh Morgan Perera Kanattage, Donato Cappetta, Fabio Rebecchi, Paolo Soda, Valerio Guarrasi, Rosa … 展开作者
Arianna Francesconi, Zhixiang Dai, Arthur Stefano Moscheni, Himesh Morgan Perera Kanattage, Donato Cappetta, Fabio Rebecchi, Paolo Soda, Valerio Guarrasi, Rosa Sicilia, Mary-Anne Hartley
备注：
7 pages, 1 figure. Submitted to Pattern Recognition Letters
Voice-based digital biomarkers can enable scalable, non-invasive screening and monitoring of Parkinson's disease (PD) and Amyotrophic Lateral Sclerosis (ALS). However, models trained on one cohort or device often fail on new acquisition settings due to cross-device and cross-cohort domain shift. This challenge is amplified in real-world scenarios with partial-label mismatch, where datasets may contain different disease labels and only partially overlap in class space. In addition, voice-based models may exploit demographic cues, raising concerns about gender-related unfairness, particularly when deployed across heterogeneous cohorts. To tackle these challenges, we propose a hybrid framework for unified three-class (healthy/PD/ALS) cross-domain voice classification from partially overlapping cohorts. The method combines style-based domain generalization with conditional adversarial alignment tailored to partial-label settings, reducing negative transfer. An additional adversarial gender branch promotes gender-invariant representations. We conduct a comprehensive evaluation across four heterogeneous sustained-vowel datasets, spanning distinct acquisition settings and devices, under both domain generalization and unsupervised domain adaptation protocols. The proposed approach is compared against twelve state-of-the-art machine learning and deep learning methods, and further evaluated through three targeted ablations, providing the first cross-cohort benchmark and end-to-end domain-adaptive framework for unified healthy/PD/ALS voice classification under partial-label mismatch and fairness constraints. Across all experimental settings, our method consistently achieves the best external generalization over the considered evaluation metrics, while maintaining reduced gender disparities. Notably, no competing method shows statistically significant gains in external performance.
NeuroVoz: a Castillian Spanish corpus of parkinsonian speech
Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid（马德里理工大学电信工程高等技术学院）；Hospital General Universitario Gregorio Marañón（格雷戈里奥·马拉尼翁大学综合医院）
BenSParX: A Robust Explainable Machine Learning Framework for Parkinson's Disease Detection from Bengali Conversational Speech
Chittagong University of Engineering and Technology（吉大港工程技术大学）；East Delta University（东三角洲大学）；Charles Sturt University（查尔斯特大学）；Bangabandhu Sheikh Mujib Medical University（班加班杜·谢赫·穆吉布医科大学）
Enhanced LSTM by Attention Mechanism for Early Detection of Parkinson's Disease through Voice Signals
Polytechnic University of Turin（都灵理工大学）；Zarqa University（扎尔卡大学）；University of Business and Technology（商业技术大学）；Memorial University of Newfoundland（纽芬兰纪念大学）
People with Parkinson's disease must be regularly monitored by their physician to observe how the disease is progressing and potentially adjust treatment plans to mitigate the symptoms. Monitoring the progression of the disease through a voice recording captured by the patient at their own home can make the process faster and less stressful. Using a dataset of voice recordings of 42 people with early-stage Parkinson's disease over a time span of 6 months, we applied multiple machine learning techniques to find a correlation between the voice recording and the patient's motor UPDRS score. We ap…
The performance of voice-based Parkinson's disease (PD) detection systems degrades when there is an acoustic mismatch between training and operating conditions caused mainly by degradation in test signals. In this paper, we address this mismatch by considering three types of degradation commonly encountered in remote voice analysis, namely background noise, reverberation and nonlinear distortion, and investigate how these degradations influence the performance of a PD detection system. Given that the specific degradation is known, we explore the effectiveness of a variety of enhancement algori…
Deep-Brain Stimulation (DBS) is a therapy used in conjunction with medication to help alleviate the motor symptoms of Parkinson's Disease (PD). However, the monitoring and adjustment of DBS settings is tedious and expensive, requiring long programming appointments every few months. We investigated the possible correlation between PD motor score severity and digitally extracted patient voice features to potentially aid clinicians in their monitoring and treatment of PD with DBS, and eventually enable a closed-loop DBS system. 5 DBS PD patients were enrolled. Voice samples were collected for var…
Somerville College, University of Oxford（牛津大学萨默维尔学院）；Aculab plc（Aculab有限公司）；Usher Institute of Population Health Sciences and Informatics, Medical School, University of Edinburgh（爱丁堡大学医学院厄舍人口健康科学与信息学研究所）
Recent studies have demonstrated that analysis of laboratory-quality voice recordings can be used to accurately differentiate people diagnosed with Parkinson's disease (PD) from healthy controls (HC). These findings could help facilitate the development of remote screening and monitoring tools for PD. In this study, we analyzed 2759 telephone-quality voice recordings from 1483 PD and 15321 recordings from 8300 HC participants. To account for variations in phonetic backgrounds, we acquired data from seven countries. We developed a statistical framework for analyzing voice, whereby we computed 3…
About 90 percent of people with Parkinson's disease (PD) experience decreased functional communication due to the presence of voice and speech disorders associated with dysarthria that can be characterized by monotony of pitch (or fundamental frequency), reduced loudness, irregular rate of speech, imprecise consonants, and changes in voice quality. Speech-language pathologists (SLPs) work with patients with PD to improve speech intelligibility using various intensive in-clinic speech treatments. SLPs also prescribe home exercises to enhance generalization of speech strategies outside of the tr…
Shanghai Maritime University（上海海事大学）；University of Technology Sydney（悉尼科技大学）
More than 90% of the Parkinson Disease (PD) patients suffer from vocal disorders. Speech impairment is already indicator of PD. This study focuses on PD diagnosis through voiceprint features. In this paper, a method based on Deep Neural Network (DNN) recognition and classification combined with Mini-Batch Gradient Descent (MBGD) is proposed to distinguish PD patients from healthy people using voiceprint features. In order to exact the voiceprint features from patients, Weighted Mel Frequency Cepstrum Coefficients (WMFCC) is applied. The proposed method is tested on experimental data obtained b…
A Benchmark for Early-stage Parkinson's Disease Detection from Speech
Centre for Language Studies, Radboud University, Nijmegen, the Netherlands（语言研究所以及拉德堡德大学，尼姆egen，荷兰）；Center of Expertise for Parkinson and Movement Disorders, Radboud University Medical Center, Nijmegen, the Netherlands（帕金森及运动障碍专家研究所，拉德堡德大学医学中心，尼姆egen，荷兰）
Parkinson's disease (PD) is a slowly progressive debilitating neurodegenerative disease which is prominently characterised by motor symptoms. Indoor localisation, including number and speed of room to room transitions, provides a proxy outcome which represents mobility and could be used as a digital biomarker to quantify how mobility changes as this disease progresses. We use data collected from 10 people with Parkinson's, and 10 controls, each of whom lived for five days in a smart home with various sensors. In order to more effectively localise them indoors, we propose a transformer-based ap…
PointExplainer: Towards Transparent Parkinson's Disease Diagnosis
Department of Mathematics: Analysis, Logic and Discrete Mathematics, Ghent University（根特大学数学系）；Department of Software Science, Faculty of Information Technologies, Tallinn University of Technology（塔林理工大学软件科学系）；Institute of Sport Sciences and Physiotherapy, University of Tartu（塔尔图大学体育科学与理疗研究所）；School of Natural Sciences and Health, Tallinn University（塔林大学自然科学与健康学院）；School of Mathematical Sciences, Queen Mary University of London（伦敦大学玛丽女王学院数学科学学院）
University of Toronto（多伦多大学）；Toronto Rehabilitation Institute, University Health Network（大学健康网络多伦多康复研究所）
This paper presents the design of a wireless sensor network for detecting and alerting the freezing of gait (FoG) symptoms in patients with Parkinson's disease. Three sensor nodes, each integrating a 3-axis accelerometer, can be placed on a patient at ankle, thigh, and truck. Each sensor node can independently detect FoG using an on-device deep learning (DL) model, featuring a squeeze and excitation convolutional neural network (CNN). In a validation using a public dataset, the prototype developed achieved a FoG detection sensitivity of 88.8% and an F1 score of 85.34%, using less than 20 k tra…
A swap-adversarial framework for improving domain generalization in electrocorticography-based Parkinson's disease classification
Department of Computer Science and Engineering, Incheon National University（仁川国立大学计算机科学与工程系）；Department of Nanobioengineering, Incheon National University（仁川国立大学纳米生物工程系）；Center for Brain-Machine Interface, Incheon National University（仁川国立大学脑机接口中心）；Department of Artificial Intelligence, Inha University（仁荷大学人工智能系）
Approximately 1.2% of the world's population has impaired voice production. As a result, automatic dysphonic voice detection has attracted considerable academic and clinical interest. However, existing methods for automated voice assessment often fail to generalize outside the training conditions or to other related applications. In this paper, we propose a deep learning framework for generating acoustic feature embeddings sensitive to vocal quality and robust across different corpora. A contrastive loss is combined with a classification loss to train our deep learning model jointly. Data warp…
Kyoto University（京都大学）；National Institute of Information and Communications Technology（国立信息与通信技术研究所）
With the development of smart devices, such as the Amazon Echo and Apple's HomePod, speech data have become a new dimension of big data. However, privacy and security concerns may hinder the collection and sharing of real-world speech data, which contain the speaker's identifiable information, i.e., voiceprint, which is considered a type of biometric identifier. Current studies on voiceprint privacy protection do not provide either a meaningful privacy-utility trade-off or a formal and rigorous definition of privacy. In this study, we design a novel and rigorous privacy metric for voiceprint p…
Paris Brain Institute - ICM（巴黎大脑研究所 - ICM）；Institut Polytechnique de Paris（巴黎综合理工学院）；Sorbonne University（索邦大学）；Concordia University（康考迪亚大学）
Many articles have used voice analysis to detect Parkinson's disease (PD), but few have focused on the early stages of the disease and the gender effect. In this article, we have adapted the latest speaker recognition system, called x-vectors, in order to detect an early stage of PD from voice analysis. X-vectors are embeddings extracted from a deep neural network, which provide robust speaker representations and improve speaker recognition when large amounts of training data are used. Our goal was to assess whether, in the context of early PD detection, this technique would outperform the mor…
Benchmarking of EEG Analysis Techniques for Parkinson's Disease Diagnosis: A Comparison between Traditional ML Methods and Foundation DL Methods
Department of Computer Science, Sapienza University of Rome（罗马大学计算机科学系）；Fondazione Ugo Bordoni（乌戈·博尔迪尼基金会）；Department of Computer Science and Mathematics, The L. H. and W. L. Collins College of Professional Studies, St. John’s University（圣约翰大学计算机科学与数学系）；University of Lorraine, CNRS, CRAN Nancy（洛林大学，CNRS，Nancy CRAN）
Belarusian State University of Informatics and Radioelectronics（白俄罗斯国立信息与无线电电子大学）；Republican Research and Clinical Center of Neurology and Neurosurgery（共和国神经学与神经外科研究临床中心）
On average the lack of biological markers causes a one year diagnostic delay to detect amyotrophic lateral sclerosis (ALS). To improve the diagnostic process an automatic voice assessment based on acoustic analysis can be used. The purpose of this work was to verify the sutability of the sustain vowel phonation test for automatic detection of patients with ALS. We proposed enhanced procedure for separation of voice signal into fundamental periods that requires for calculation of perturbation measurements (such as jitter and shimmer). Also we proposed method for quantitative assessment of patho…
The emergence of voice-assistant devices ushers in delightful user experiences not just on the smart home front, but also in diverse educational environments from classrooms to personalized-learning/tutoring. However, the use of voice as an interaction modality also could result in exposure of user's identity, and hinders the broader adoption of voice interfaces; this is especially important in environments where children are present and their voice privacy needs to be protected. To this end, building on state-of-the-art techniques proposed in the literature, we design and evaluate a practical…
