Recently proposed automatic pathological speech classification techniques use unsupervised auto-encoders to obtain a high-level abstract representation of speech. Since these representations are learned based on reconstructing the input, there is no guarantee that they are robust to pathology-unrelated cues such as speaker identity information. Further, these representations are not necessarily discriminative for pathology detection. In this paper, we exploit supervised auto-encoders to extract robust and discriminative speech representations for Parkinson's disease classification. To reduce the influence of speaker variabilities unrelated to pathology, we propose to obtain speaker identity-invariant representations by adversarial training of an auto-encoder and a speaker identification task. To obtain a discriminative representation, we propose to jointly train an auto-encoder and a pathological speech classifier. Experimental results on a Spanish database show that the proposed supervised representation learning methods yield more robust and discriminative representations for automatically classifying Parkinson's disease speech, outperforming the baseline unsupervised representation learning system.
Parkinson's disease is a neurodegenerative disorder characterized by the presence of different motor impairments. Information from speech, handwriting, and gait signals have been considered to evaluate the neurological state of the patients. On the other hand, user models based on Gaussian mixture models - universal background models (GMM-UBM) and i-vectors are considered the state-of-the-art in biometric applications like speaker verification because they are able to model specific speaker traits. This study introduces the use of GMM-UBM and i-vectors to evaluate the neurological state of Par…
Speech disorders often occur at the early stage of Parkinson's disease (PD). The speech impairments could be indicators of the disorder for early diagnosis, while motor symptoms are not obvious. In this study, we constructed a new speech corpus of Mandarin Chinese and addressed classification of patients with PD. We implemented classical machine learning methods with ranking algorithms for feature selection, convolutional and recurrent deep networks, and an end to end system. Our classification accuracy significantly surpassed state-of-the-art studies. The result suggests that free talk has st…
Assessing Progress of Parkinson s Disease Using Acoustic Analysis of Phonation
Brno University of Technology（布尔诺理工大学）；St. Anne’s University Hospital（圣安妮大学医院）；Central European Institute of Technology（中欧技术研究所）；Masaryk University（马萨里克大学）；Faculty Hospital（大学医院）；Escola Universitaria Politecnica de Mataro（马塔罗大学理工学院）；University of the Basque Country UPV/EHU（巴斯克大学（UPV/EHU））；Institute for Technological Development and Innovation in Communications (IDeTIC)（通信技术发展与创新研究所（IDeTIC））；University of Las Palmas de Gran Canaria（大加那利岛拉斯帕尔马斯大学）
On the Relevance of Clinical Assessment Tasks for the Automatic Detection of Parkinson's Disease Medication State from Speech
Universitat Politècnica de València（瓦伦西亚理工大学）；Universidade de Lisboa（里斯本大学）；Faculdade de Medicina, Universidade de Lisboa（里斯本大学医学院）；PRHLT INESC-ID；Instituto Superior Técnico（技术高等学院）；Laboratory of Clinical Pharmacology and Therapeutics（临床药理与治疗实验室）；CNS - Campus Neurológico Alcoitão（神经学中心-阿尔科伊托昂校园）；Health School of Sciences（健康科学学院）
Parkinson disease (PD)'s speech recognition is an effective way for its diagnosis, which has become a hot and difficult research area in recent years. As we know, there are large corpuses (segments) within one subject. However, too large segments will increase the complexity of the classification model. Besides, the clinicians interested in finding diagnostic speech markers that reflect the pathology of the whole subject. Since the optimal relevant features of each speech sample segment are different, it is difficult to find the uniform diagnostic speech markers. Therefore, it is necessary to …
Evaluating Parkinson's Disease Detection in Anonymized Speech: A Performance and Acoustic Analysis
German Research Center for Artificial Intelligence（德国人工智能研究中心）；INESC-ID；Technical University of Berlin（柏林技术大学）；Instituto Superior Técnico, University of Lisbon（里斯本大学理工学院）
Zero-Shot Parkinson's Disease Detection from Speech: Comparing Large Audio and Language Models
School of Computing, Mathematics and Engineering, Charles Sturt University（计算机科学与工程学院，查尔斯·斯图尔特大学）；Department of Computer Science, Rensselaer Polytechnic Institute（计算机科学系，伦塞拉尔理工学院）
Parkinson's Disease Detection through Vocal Biomarkers and Advanced Machine Learning Algorithms
New Jersey City University（新泽西城市大学）；University of South Dakota（南达科他大学）；Monroe College（门罗学院）；University of Texas at Dallas（德州大学达拉斯分校）；International American University（国际美国大学）；Gannon University（甘农大学）；Saint Francis College（圣弗朗西斯学院）
Brno University of Technology（布尔诺理工大学）；St. Anne’s University Hospital（圣安妮大学医院）；Masaryk University（马萨里克大学）；Escola Universitaria Politecnica de Mataro（马塔罗理工学院）；University of the Basque Country UPV/EHU（巴斯克大学）
Up to 90% of patients with Parkinson's disease (PD) suffer from hypokinetic dysathria (HD) which is also manifested in the field of phonation. Clinical signs of HD like monoloudness, monopitch or hoarse voice are usually quantified by conventional clinical interpretable features (jitter, shimmer, harmonic-to-noise ratio, etc.). This paper provides large and robust insight into perceptual analysis of 5 Czech vowels of 84 PD patients and proves that despite the clinical inexplicability the perceptual features outperform the conventional ones, especially in terms of discrimination power (classifi…
Adapting Self-Supervised Speech Representations for Cross-lingual Dysarthria Detection in Parkinson's Disease
FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；UT Austin（奥斯汀大学）；CMU（卡内基梅隆大学）；Czech Technical University in Prague（布拉格技术大学）；Idiap Research Institute（Idiap研究机构）；Universidad de Antioquia（安提奥基亚大学）；Shenzhen Loop Area Institute（深圳河套学院）；Fortemedia（Fortemedia公司）
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…
Parkinson's disease (PD) is a progressive degenerative disorder of the central nervous system characterized by motor and non-motor symptoms. As the disease progresses, patients alternate periods in which motor symptoms are mitigated due to medication intake (ON state) and periods with motor complications (OFF state). The time that patients spend in the OFF condition is currently the main parameter employed to assess pharmacological interventions and to evaluate the efficacy of different active principles. In this work, we present a system that combines automatic speech processing and deep lear…
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）
Investigating the Effectiveness of Explainability Methods in Parkinson's Detection from Speech
University of Bologna（博洛尼亚大学）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）；Mila-Québec AI Institute（米拉-魁北克人工智能研究所）；Concordia University（康考迪亚大学）；Laval University（拉瓦尔大学）；Université de Montréal（蒙特利尔大学）
Carnegie Mellon University Africa（卡内基梅隆大学非洲分校）；Carnegie Mellon University（卡内基梅隆大学）；INSOFE（国际工程与科学教育研究所）
One of the symptoms observed in the early stages of Parkinson's Disease (PD) is speech impairment. Speech disorders can be used to detect this disease before it degenerates. This work analyzes speech features and machine learning approaches to predict PD. Acoustic features such as shimmer and jitter variants, and Mel Frequency Cepstral Coefficients (MFCC) are extracted from speech signals. We use two datasets in this work: the MDVR-KCL and the Italian Parkinson's Voice and Speech database. To separate PD and non-PD speech signals, seven classification models were implemented: K-Nearest Neighbo…
RECA-PD: A Robust Explainable Cross-Attention Method for Speech-based Parkinson's Disease Classification
Centre for Language Studies, Radboud University, Nijmegen, the Netherlands（语言研究中心，拉德堡德大学，尼迈根，荷兰）；Center of Expertise for Parkinson and Movement Disorders, Radboud University Medical Center, Nijmegen, the Netherlands（帕金森病与运动障碍专家中心，拉德堡德大学医学中心，尼迈根，荷兰）
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）
Universidad de Antioquia UdeA（安蒂奥基亚大学）；Czech Technical University in Prague（布拉格捷克技术大学）
Parkinson's disease patients develop different speech impairments that affect their communication capabilities. The automatic assessment of the speech of the patients allows the development of computer aided tools to support the diagnosis and the evaluation of the disease severity. This paper introduces a methodology to classify Parkinson's disease from speech in three different languages: Spanish, German, and Czech. The proposed approach considers convolutional neural networks trained with time frequency representations and a transfer learning strategy among the three languages. The transfer …
University of Rochester（罗切斯特大学）；BUET（孟加拉国工程技术大学）；University of Rochester Medical Center（罗切斯特大学医学中心）；University of Birmingham（伯明翰大学）；MIT（麻省理工学院）
In this paper, we envision a web-based framework that can help anyone, anywhere around the world record a short speech task, and analyze the recorded data to screen for Parkinson's disease (PD). We collected data from 726 unique participants (262 PD, 38% female; 464 non-PD, 65% female; average age: 61) -- from all over the US and beyond. A small portion of the data was collected in a lab setting to compare quality. The participants were instructed to utter a popular pangram containing all the letters in the English alphabet "the quick brown fox jumps over the lazy dog..". We extracted both sta…
Semnan University（塞姆南大学）；Amirkabir University of Technology (Tehran Polytechnic)（阿米尔卡比尔理工大学（德黑兰理工大学））
Background and Objective: Parkinson's disease (PD) is the second most common progressive neurological condition after Alzheimer's, characterized by motor and non-motor symptoms. Developing a method to diagnose the condition in its beginning phases is essential because of the significant number of individuals afflicting with this illness. PD is typically identified using motor symptoms or other Neuroimaging techniques, such as DATSCAN and SPECT. These methods are expensive, time-consuming, and unavailable to the general public; furthermore, they are not very accurate. These constraints encourag…
From Black Box to Biomarker: Sparse Autoencoders for Interpreting Speech Models of Parkinson's Disease
McGill University（麦吉尔大学）；Centre for Research on Brain, Language, and Music（大脑、语言和音乐研究中心）；Mila - Quebec AI Institute（魁北克人工智能研究所）；Douglas Research Centre（道格拉斯研究中心）；Concordia University（Concordia大学）
Motor, Cognitive, or Corpus? What Survives Cross-Lingual Transfer in Speech-Based Parkinsons Disease Detection
Hertie Institute for AI in Brain Health, University of Tübingen（蒂宾根大学赫蒂脑健康人工智能研究所）；Tübingen AI Center, University of Tübingen（蒂宾根大学蒂宾根人工智能中心）；Charité–Universitätsmedizin（夏里特医学院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）
Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languages
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；University Hospital RWTH Aachen（亚琛工业大学医院）；University of Antioquia（安蒂奥基亚大学）；Czech Technical University in Prague（布拉格捷克理工大学）
