Speech production is a complex phenomenon, wherein the brain orchestrates a sequence of processes involving thought processing, motor planning, and the execution of articulatory movements. However, this intricate execution of various processes is susceptible to influence and disruption by various neurodegenerative pathological speech disorders, such as Parkinsons' disease, resulting in dysarthria, apraxia, and other conditions. These disorders lead to pathological speech characterized by abnormal speech patterns and imprecise articulation. Diagnosing these speech disorders in clinical settings typically involves auditory perceptual tests, which are time-consuming, and the diagnosis can vary among clinicians based on their experiences, biases, and cognitive load during the diagnosis. Additionally, unlike neurotypical speakers, patients with speech pathologies or impairments are unable to access various virtual assistants such as Alexa, Siri, etc. To address these challenges, several automatic pathological speech detection (PSD) approaches have been proposed. These approaches aim to provide efficient and accurate detection of speech disorders, thereby facilitating timely intervention and support for individuals affected by these conditions. These approaches mainly vary in two aspects: the input representations utilized and the classifiers employed. Due to the limited availability of data, the performance of detection remains subpar. Self-supervised learning (SSL) embeddings, such as wav2vec2, and their multilingual versions, are being explored as a promising avenue to improve performance. These embeddings leverage self-supervised learning techniques to extract rich representations from audio data, thereby offering a potential solution to address the limitations posed by the scarcity of labeled data.
Robust and Complex Approach of Pathological Speech Signal Analysis
Brno University of Technology（布尔诺理工大学）；Masaryk University（马萨里克大学）；Universidad Politecnica de Madrid（马德里理工大学）；St. Anne’s University Hospital（圣安妮大学医院）；Central European Institute of Technology（中欧理工学院）
An Objective Evaluation Framework for Pathological Speech Synthesis
University of Amsterdam（阿姆斯特丹大学）；Delft University of Technology（代尔夫特理工大学）；Netherlands Cancer Institute（荷兰癌症研究所）；Idiap Research Institute（伊迪亚普研究所）；École polytechnique fédérale de Lausanne（洛桑联邦理工学院）
Pathological voice adaptation with autoencoder-based voice conversion
Delft University of Technology（代尔夫特理工大学）；Universitat Politecnica de Catalunya（加泰罗尼亚理工大学）；University of Amsterdam（阿姆斯特丹大学）；Netherlands Cancer Institute（荷兰癌症研究所）；Johns Hopkins University（约翰斯·霍普金斯大学）
Multi-class Detection of Pathological Speech with Latent Features: How does it perform on unseen data?
Technische Hochschule Nürnberg Georg Simon Ohm（纽伦堡乔治·西蒙·欧姆应用科学大学）；Friedrich-Alexander-University Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Intel Labs（英特尔研究院）
本研究利用预训练 wav2vec 2.0 的潜在表示，结合神经网络、大间隔分类器和树提升机区分四种病理语音，在噪声和未见数据下展现出强鲁棒性与高 F1 分数。
Perceptual implications of automatic anonymization in pathological speech
Pattern Recognition Lab, Friedrich-Alexander-Universit\"at Erlangen-N\"urnberg, Erlangen, Germany. Department of Urology, Stanford University, Stanford, CA, USA. Department of Radiology, Stanford University, Stanford, CA, USA. Lab for AI in Medicine, RWTH Aachen University, Aachen, Germany. Department of Diagnostic；Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany. Institute of Radiology, University Hospital Erlangen, Erlangen, Germany. Department of Foreign Language Education, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany. Department of Otorhinolaryngology, Head；Neck Surgery, Ludwig-Maximilians-Universität München, Munich, Germany. Speech \& Language Processing Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
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）
The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech
Winterlight Labs（Winterlight实验室）；University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（Vector人工智能研究所）；Li Ka Shing Knowledge Institute, St Michael’s Hospital（圣迈克尔医院李嘉诚知识研究所）；Surgical Safety Technologies Inc.（外科安全技术公司）
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）
