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）
Large audio and language models have recently demonstrated zero-shot reasoning capabilities across various domains. However, it remains unclear how the form of audio input, whether handcrafted acoustic features extracted from speech or the raw audio waveform itself, affects performance for Parkinson's disease (PD) detection across different languages. In this study, we systematically compare two input modalities for zero-shot PD detection: (i) handcrafted acoustic features extracted from speech recordings analyzed by a general-purpose LLM, and (ii) direct waveform input analyzed by audio-capable models. Experiments on PD speech datasets in four languages show that performance varies across input modalities, speech tasks, and languages. Handcrafted acoustic features provide more stable performance in a low-resource language (e.g., Bengali), whereas audio input yields dataset-dependent gains. These findings highlight the impact of input modality on zero-shot PD detection from speech.
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…
Idiap Research Institute（伊迪亚普研究所）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
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 t…
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）
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）
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）
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…
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）
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'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…
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…
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…
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…
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…
