UNASSIGNED: Dysarthria, a motor speech disorder caused by muscle weakness or paralysis, severely impacts speech intelligibility and quality of life. The condition is prevalent in motor speech disorders such as Parkinson\'s disease (PD), atypical parkinsonism such as progressive supranuclear palsy (PSP), Huntington\'s disease (HD), and amyotrophic lateral sclerosis (ALS). Improving intelligibility is not only an outcome that matters to patients but can also play a critical role as an endpoint in clinical research and drug development. This study validates a digital measure for speech intelligibility, the ki: SB-M intelligibility score, across various motor speech disorders and languages following the Digital Medicine Society (DiMe) V3 framework.
UNASSIGNED: The study used four datasets: healthy controls (HCs) and patients with PD, HD, PSP, and ALS from Czech, Colombian, and German populations. Participants\' speech intelligibility was assessed using the ki: SB-M intelligibility score, which is derived from automatic speech recognition (ASR) systems. Verification with inter-ASR reliability and temporal consistency, analytical validation with correlations to gold standard clinical dysarthria scores in each disease, and clinical validation with group comparisons between HCs and patients were performed.
UNASSIGNED: Verification showed good to excellent inter-rater reliability between ASR systems and fair to good consistency. Analytical validation revealed significant correlations between the SB-M intelligibility score and established clinical measures for speech impairments across all patient groups and languages. Clinical validation demonstrated significant differences in intelligibility scores between pathological groups and healthy controls, indicating the measure\'s discriminative capability.
UNASSIGNED: The ki: SB-M intelligibility score is a reliable, valid, and clinically relevant tool for assessing speech intelligibility in motor speech disorders. It holds promise for improving clinical trials through automated, objective, and scalable assessments. Future studies should explore its utility in monitoring disease progression and therapeutic efficacy as well as add data from further dysarthrias to the validation.

BACKGROUND: Human health is a complex, dynamic concept encompassing a spectrum of states influenced by genetic, environmental, physiological, and psychological factors. Traditional Chinese Medicine categorizes health into nine body constitutional types, each reflecting unique balances or imbalances in vital energies, influencing physical, mental, and emotional states. Advances in machine learning models offer promising avenues for diagnosing conditions like Alzheimer\'s, dementia, and respiratory diseases by analyzing speech patterns, enabling complementary non-invasive disease diagnosis. The study aims to use speech audio to identify subhealth populations characterized by unbalanced constitution types.
METHODS: Participants, aged 18-45, were selected from the Acoustic Study of Health. Audio recordings were collected using ATR2500X-USB microphones and Praat software. Exclusion criteria included recent illness, dental issues, and specific medical histories. The audio data were preprocessed to Mel-frequency cepstral coefficients (MFCCs) for model training. Three deep learning models-1-Dimensional Convolution Network (Conv1D), 2-Dimensional Convolution Network (Conv2D), and Long Short-Term Memory (LSTM)-were implemented using Python to classify health status. Saliency maps were generated to provide model explainability.
RESULTS: The study used 1,378 recordings from balanced (healthy) and 1,413 from unbalanced (subhealth) types. The Conv1D model achieved a training accuracy of 91.91% and validation accuracy of 84.19%. The Conv2D model had 96.19% training accuracy and 84.93% validation accuracy. The LSTM model showed 92.79% training accuracy and 87.13% validation accuracy, with early signs of overfitting. AUC scores were 0.92 and 0.94 (Conv1D), 0.99 (Conv2D), and 0.97 (LSTM). All models demonstrated robust performance, with Conv2D excelling in discrimination accuracy.
CONCLUSIONS: The deep learning classification of human speech audio for health status using body constitution types showed promising results with Conv1D, Conv2D, and LSTM models. Analysis of ROC curves, training accuracy, and validation accuracy showed all models robustly distinguished between balanced and unbalanced constitution types. Conv2D excelled with good accuracy, while Conv1D and LSTM also performed well, affirming their reliability. The study integrates constitution theory and deep learning technologies to classify subhealth populations using noninvasive approach, thereby promoting personalized medicine and early intervention strategies.

UNASSIGNED: Dementia is a general term for several progressive neurodegenerative disorders including Alzheimer\'s disease. Timely and accurate detection is crucial for early intervention. Advancements in artificial intelligence present significant potential for using machine learning to aid in early detection.
UNASSIGNED: Summarize the state-of-the-art machine learning-based approaches for dementia prediction, focusing on non-invasive methods, as the burden on the patients is lower. Specifically, the analysis of gait and speech performance can offer insights into cognitive health through clinically cost-effective screening methods.
UNASSIGNED: A systematic literature review was conducted following the PRISMA protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The search was performed on three electronic databases (Scopus, Web of Science, and PubMed) to identify the relevant studies published between 2017 to 2022. A total of 40 papers were selected for review.
UNASSIGNED: The most common machine learning methods employed were support vector machine followed by deep learning. Studies suggested the use of multimodal approaches as they can provide comprehensive and better prediction performance. Deep learning application in gait studies is still in the early stages as few studies have applied it. Moreover, including features of whole body movement contribute to better classification accuracy. Regarding speech studies, the combination of different parameters (acoustic, linguistic, cognitive testing) produced better results.
UNASSIGNED: The review highlights the potential of machine learning, particularly non-invasive approaches, in the early prediction of dementia. The comparable prediction accuracies of manual and automatic speech analysis indicate an imminent fully automated approach for dementia detection.

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Speech impairments often emerge as one of the primary indicators of Parkinson\'s disease (PD), albeit not readily apparent in its early stages. While previous studies focused predominantly on binary PD detection, this research explored the use of deep learning models to automatically classify sustained vowel recordings into healthy controls, mild PD, or severe PD based on motor symptom severity scores. Popular convolutional neural network (CNN) architectures, VGG and ResNet, as well as vision transformers, Swin, were fine-tuned on log mel spectrogram image representations of the segmented voice data. Furthermore, the research investigated the effects of audio segment lengths and specific vowel sounds on the performance of these models. The findings indicated that implementing longer segments yielded better performance. The models showed strong capability in distinguishing PD from healthy subjects, achieving over 95% precision. However, reliably discriminating between mild and severe PD cases remained challenging. The VGG16 achieved the best overall classification performance with 91.8% accuracy and the largest area under the ROC curve. Furthermore, focusing analysis on the vowel /u/ could further improve accuracy to 96%. Applying visualization techniques like Grad-CAM also highlighted how CNN models focused on localized spectrogram regions while transformers attended to more widespread patterns. Overall, this work showed the potential of deep learning for non-invasive screening and monitoring of PD progression from voice recordings, but larger multi-class labeled datasets are needed to further improve severity classification.

Parkinson\'s disease (PD) is a neurodegenerative disorder characterized by a range of motor and non-motor symptoms. One of the notable non-motor symptoms of PD is the presence of vocal disorders, attributed to the underlying pathophysiological changes in the neural control of the laryngeal and vocal tract musculature. From this perspective, the integration of machine learning (ML) techniques in the analysis of speech signals has significantly contributed to the detection and diagnosis of PD. Particularly, MEL Frequency Cepstral Coefficients (MFCCs) and Gammatone Frequency Cepstral Coefficients (GTCCs) are both feature extraction techniques commonly used in the field of speech and audio signal processing that could exhibit great potential for vocal disorder identification. This study presents a novel approach to the early detection of PD through ML applied to speech analysis, leveraging both MFCCs and GTCCs. The recordings contained in the Mobile Device Voice Recordings at King\'s College London (MDVR-KCL) dataset were used. These recordings were collected from healthy individuals and PD patients while they read a passage and during a spontaneous conversation on the phone. Particularly, the speech data regarding the spontaneous dialogue task were processed through speaker diarization, a technique that partitions an audio stream into homogeneous segments according to speaker identity. The ML applied to MFCCS and GTCCs allowed us to classify PD patients with a test accuracy of 92.3%. This research further demonstrates the potential to employ mobile phones as a non-invasive, cost-effective tool for the early detection of PD, significantly improving patient prognosis and quality of life.

This study assessed the influence of speaker similarity and sample length on the performance of an automatic speaker recognition (ASR) system utilizing the SpeechBrain toolkit. The dataset comprised recordings from 20 male identical twin speakers engaged in spontaneous dialogues and interviews. Performance evaluations involved comparing identical twins, all speakers in the dataset (including twin pairs), and all speakers excluding twin pairs. Speech samples, ranging from 5 to 30 s, underwent assessment based on equal error rates (EER) and Log cost-likelihood ratios (Cllr). Results highlight the substantial challenge posed by identical twins to the ASR system, leading to a decrease in overall speaker recognition accuracy. Furthermore, analyses based on longer speech samples outperformed those using shorter samples. As sample size increased, standard deviation values for both intra and inter-speaker similarity scores decreased, indicating reduced variability in estimating speaker similarity/dissimilarity levels in longer speech stretches compared to shorter ones. The study also uncovered varying degrees of likeness among identical twins, with certain pairs presenting a greater challenge for ASR systems. These outcomes align with prior research and are discussed within the context of relevant literature.

OBJECTIVE: A clinical decision tool for Transient Loss of Consciousness (TLOC) could reduce currently high misdiagnosis rates and waiting times for specialist assessments. Most clinical decision tools based on patient-reported symptom inventories only distinguish between two of the three most common causes of TLOC (epilepsy, functional /dissociative seizures, and syncope) or struggle with the particularly challenging differentiation between epilepsy and FDS. Based on previous research describing differences in spoken accounts of epileptic seizures and FDS seizures, this study explored the feasibility of predicting the cause of TLOC by combining the automated analysis of patient-reported symptoms and spoken TLOC descriptions.
METHODS: Participants completed an online web application that consisted of a 34-item medical history and symptom questionnaire (iPEP) and spoken interaction with a virtual agent (VA) that asked eight questions about the most recent experience of TLOC. Support Vector Machines (SVM) were trained using different combinations of features and nested leave-one-out cross validation. The iPEP provided a baseline performance. Inspired by previous qualitative research three spoken language based feature sets were designed to assess: (1) formulation effort, (2) the proportion of words from different semantic categories, and (3) verb, adverb, and adjective usage.
RESULTS: 76 participants completed the application (Epilepsy = 24, FDS = 36, syncope = 16). Only 61 participants also completed the VA interaction (Epilepsy = 20, FDS = 29, syncope = 12). The iPEP model accurately predicted 65.8 % of all diagnoses, but the inclusion of the language features increased the accuracy to 85.5 % by improving the differential diagnosis between epilepsy and FDS.
CONCLUSIONS: These findings suggest that an automated analysis of TLOC descriptions collected using an online web application and VA could improve the accuracy of current clinical decisions tools for TLOC and facilitate clinical stratification processes (such as ensuring appropriate referral to cardiological versus neurological investigation and management pathways).

COVID-19, an infectious disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organisation (WHO) in March 2020. By mid-August 2020, more than 21 million people have tested positive worldwide. Infections have been growing rapidly and tremendous efforts are being made to fight the disease. In this paper, we attempt to systematise the various COVID-19 research activities leveraging data science, where we define data science broadly to encompass the various methods and tools-including those from artificial intelligence (AI), machine learning (ML), statistics, modeling, simulation, and data visualization-that can be used to store, process, and extract insights from data. In addition to reviewing the rapidly growing body of recent research, we survey public datasets and repositories that can be used for further work to track COVID-19 spread and mitigation strategies. As part of this, we present a bibliometric analysis of the papers produced in this short span of time. Finally, building on these insights, we highlight common challenges and pitfalls observed across the surveyed works. We also created a live resource repository at https://github.com/Data-Science-and-COVID-19/Leveraging-Data-Science-To-Combat-COVID-19-A-Comprehensive-Review that we intend to keep updated with the latest resources including new papers and datasets.

To examine the association between speech and facial features with depression, anxiety, and apathy in older adults with mild cognitive impairment (MCI).
Speech and facial expressions of 319 MCI patients were digitally recorded via audio and video recording software. Three of the most common neuropsychiatric symptoms (NPS) were evaluated by the Public Health Questionnaire, General Anxiety Disorder, and Apathy Evaluation Scale, respectively. Speech and facial features were extracted using the open-source data analysis toolkits. Machine learning techniques were used to validate the diagnostic power of extracted features.
Different speech and facial features were associated with specific NPS. Depression was associated with spectral and temporal features, anxiety and apathy with frequency, energy, spectral, and temporal features. Additionally, depression was associated with facial features (action unit, AU) 10, 12, 15, 17, 25, anxiety with AU 10, 15, 17, 25, 26, 45, and apathy with AU 5, 26, 45. Significant differences in speech and facial features were observed between males and females. Based on machine learning models, the highest accuracy for detecting depression, anxiety, and apathy reached 95.8%, 96.1%, and 83.3% for males, and 87.8%, 88.2%, and 88.6% for females, respectively.
Depression, anxiety, and apathy were characterized by distinct speech and facial features. The machine learning model developed in this study demonstrated good classification in detecting depression, anxiety, and apathy. A combination of audio and video may provide objective methods for the precise classification of these symptoms.