A Longitudinal Normative Dataset and Protocol for Speech and Language Biomarker Research

Schwoebel, James W.; Schwartz, Jeffrey; Warrenburg, Lindsay; Brown, Roland; Awasthi, Ashi; New, Austin; Butler, Monroe; Moss, Mark B.; Pissadaki, Eleftheria Kyriaki

doi:10.1101/2021.08.16.21262125

Cited by 5 publications

(3 citation statements)

References 53 publications

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“…After literature screening for this review, a publication was released, which showcases the highlighted points for everyday-life data collection ( 81 ). The authors managed to gather voice samples via a web app of over 6,650 participants, of which roughly 10 % reported to be depressed.…”

Section: Discussionmentioning

confidence: 99%

Voice Analysis for Neurological Disorder Recognition–A Systematic Review and Perspective on Emerging Trends

Hecker

Steckhan

Eyben

et al. 2022

Front. Digit. Health

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Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.

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Section: Discussionmentioning

confidence: 99%

Voice Analysis for Neurological Disorder Recognition–A Systematic Review and Perspective on Emerging Trends

Hecker

Steckhan

Eyben

et al. 2022

Front. Digit. Health

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“…This step consists of voice data collection coupled with well-documented clinical data in screening platforms such as Colive Voice [ 30 ] or large prospective cohort studies [ 2 , 31 ]. The collected data have to be diverse enough and should represent the target population in terms of languages, accents, and socioeconomic backgrounds to decrease the risk of systemic biases and the risk of increasing a potential preexisting digital and socioeconomic divide in the population.…”

Section: Development Of a Digital Health Solution Based On Vocal Biom...mentioning

confidence: 99%

Recommendations for Successful Implementation of the Use of Vocal Biomarkers for Remote Monitoring of COVID-19 and Long COVID in Clinical Practice and Research

Fischer¹,

Elbéji²,

Aguayo³

et al. 2022

Interact J Med Res

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The COVID-19 pandemic accelerated the use of remote patient monitoring in clinical practice or research for safety and emergency reasons, justifying the need for innovative digital health solutions to monitor key parameters or symptoms related to COVID-19 or Long COVID. The use of voice-based technologies, and in particular vocal biomarkers, is a promising approach, voice being a rich, easy-to-collect medium with numerous potential applications for health care, from diagnosis to monitoring. In this viewpoint, we provide an overview of the potential benefits and limitations of using voice to monitor COVID-19, Long COVID, and related symptoms. We then describe an optimal pipeline to bring a vocal biomarker candidate from research to clinical practice and discuss recommendations to achieve such a clinical implementation successfully.

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“…The present study aims to create large datasets of the Japanese language with a great number of samples, types, and amount of utterances and look into ways to extract linguistic and audio indicators that can be used to differentiate between healthy-disease and disease-disease. Such datasets are limited at present ( 38 ). They could enable us to obtain reliable results and bring pioneering insights from NLP in the Japanese-speaking region.…”

Section: Introductionmentioning

confidence: 99%

Understanding psychiatric illness through natural language processing (UNDERPIN): Rationale, design, and methodology

et al. 2022

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IntroductionPsychiatric disorders are diagnosed through observations of psychiatrists according to diagnostic criteria such as the DSM-5. Such observations, however, are mainly based on each psychiatrist's level of experience and often lack objectivity, potentially leading to disagreements among psychiatrists. In contrast, specific linguistic features can be observed in some psychiatric disorders, such as a loosening of associations in schizophrenia. Some studies explored biomarkers, but biomarkers have yet to be used in clinical practice.AimThe purposes of this study are to create a large dataset of Japanese speech data labeled with detailed information on psychiatric disorders and neurocognitive disorders to quantify the linguistic features of those disorders using natural language processing and, finally, to develop objective and easy-to-use biomarkers for diagnosing and assessing the severity of them.MethodsThis study will have a multi-center prospective design. The DSM-5 or ICD-11 criteria for major depressive disorder, bipolar disorder, schizophrenia, and anxiety disorder and for major and minor neurocognitive disorders will be regarded as the inclusion criteria for the psychiatric disorder samples. For the healthy subjects, the absence of a history of psychiatric disorders will be confirmed using the Mini-International Neuropsychiatric Interview (M.I.N.I.). The absence of current cognitive decline will be confirmed using the Mini-Mental State Examination (MMSE). A psychiatrist or psychologist will conduct 30-to-60-min interviews with each participant; these interviews will include free conversation, picture-description task, and story-telling task, all of which will be recorded using a microphone headset. In addition, the severity of disorders will be assessed using clinical rating scales. Data will be collected from each participant at least twice during the study period and up to a maximum of five times at an interval of at least one month.DiscussionThis study is unique in its large sample size and the novelty of its method, and has potential for applications in many fields. We have some challenges regarding inter-rater reliability and the linguistic peculiarities of Japanese. As of September 2022, we have collected a total of >1000 records from >400 participants. To the best of our knowledge, this data sample is one of the largest in this field.Clinical Trial RegistrationIdentifier: UMIN000032141.

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A Longitudinal Normative Dataset and Protocol for Speech and Language Biomarker Research

Cited by 5 publications

References 53 publications

Voice Analysis for Neurological Disorder Recognition–A Systematic Review and Perspective on Emerging Trends

Voice Analysis for Neurological Disorder Recognition–A Systematic Review and Perspective on Emerging Trends

Recommendations for Successful Implementation of the Use of Vocal Biomarkers for Remote Monitoring of COVID-19 and Long COVID in Clinical Practice and Research

Understanding psychiatric illness through natural language processing (UNDERPIN): Rationale, design, and methodology

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