Artificial Intelligence Methods for Assessing Speech, Language, and Communication Functioning

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neurolinguistics".

Deadline for manuscript submissions: 25 May 2024 | Viewed by 2662

Special Issue Editors


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Guest Editor
ISP, University of Oslo, 0371 Oslo, Norway
Interests: machine learning; natural language processing; signal processing; speech and language impairment diagnosis and treatment

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Guest Editor
Department of Neurology, The Johns Hopkins University, Baltimore, MD 21210, USA
Interests: dementia; primary aphasias; primary progressive aphasias; speech and language dis-orders after stroke; transcranial direct current stimulation

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Guest Editor
Department of Neurology, The Johns Hopkins University, Baltimore, MD 21210, USA
Interests: cognitive neuroscience; speech and language diagnosis and treatment; neuroimaging; written language production

Special Issue Information

Dear Colleagues,

Computational methods for language assessment have become increasingly important in recent years as they offer new possibilities for measuring and enhancing speech, language, and communication skills in various clinical populations. Among these methods, artificial intelligence (AI), machine learning, natural language processing, and signal processing can provide objective and reliable indicators of speech and language functioning, which can inform the diagnosis, prognosis, and treatment evaluation of patients with neurocognitive disorders, such as aphasia and speech impairments caused by stroke, dementia, or traumatic brain injury.

This Special Issue aims to showcase the latest developments and applications of computational language assessment in this domain. We invite submissions of original research articles, reviews, or protocol papers that present novel algorithms and models for assessing and scoring speech and language performance in patients with neurocognitive conditions. We also welcome studies that demonstrate the validity, reliability, and security of these methods, as well as their implications for clinical practice and education. This Special Issue will contribute to the advancement of computational neurocognitive and neurolinguistic assessment research and its impact on society.

Dr. Charalambos Themistocleous
Dr. Kyrana Tsapkini
Dr. Kyriaki Neophytou
Guest Editors

Manuscript Submission Information

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Keywords

  • machine learning
  • natural language processing
  • signal processing
  • aphasia
  • speech and language disorders
  • computational language assessment (CLA)

Published Papers (2 papers)

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Research

21 pages, 2643 KiB  
Article
A Methodological Approach to Quantifying Silent Pauses, Speech Rate, and Articulation Rate across Distinct Narrative Tasks: Introducing the Connected Speech Analysis Protocol (CSAP)
by Georgia Angelopoulou, Dimitrios Kasselimis, Dionysios Goutsos and Constantin Potagas
Brain Sci. 2024, 14(5), 466; https://doi.org/10.3390/brainsci14050466 - 7 May 2024
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Abstract
The examination of connected speech may serve as a valuable tool for exploring speech output in both healthy speakers and individuals with language disorders. Numerous studies incorporate various fluency and silence measures into their analyses to investigate speech output patterns in different populations, [...] Read more.
The examination of connected speech may serve as a valuable tool for exploring speech output in both healthy speakers and individuals with language disorders. Numerous studies incorporate various fluency and silence measures into their analyses to investigate speech output patterns in different populations, along with the underlying cognitive processes that occur while speaking. However, methodological inconsistencies across existing studies pose challenges in comparing their results. In the current study, we introduce CSAP (Connected Speech Analysis Protocol), which is a specific methodological approach to investigate fluency metrics, such as articulation rate and speech rate, as well as silence measures, including silent pauses’ frequency and duration. We emphasize the importance of employing a comprehensive set of measures within a specific methodological framework to better understand speech output patterns. Additionally, we advocate for the use of distinct narrative tasks for a thorough investigation of speech output in different conditions. We provide an example of data on which we implement CSAP to showcase the proposed pipeline. In conclusion, CSAP offers a comprehensive framework for investigating speech output patterns, incorporating fluency metrics and silence measures in distinct narrative tasks, thus allowing a detailed quantification of connected speech in both healthy and clinical populations. We emphasize the significance of adopting a unified methodological approach in connected speech studies, enabling the integration of results for more robust and generalizable conclusions. Full article
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11 pages, 3832 KiB  
Article
Using Objective Speech Analysis Techniques for the Clinical Diagnosis and Assessment of Speech Disorders in Patients with Multiple Sclerosis
by Zeynep Z. Sonkaya, Bilgin Özturk, Rıza Sonkaya, Esra Taskiran and Ömer Karadas
Brain Sci. 2024, 14(4), 384; https://doi.org/10.3390/brainsci14040384 - 16 Apr 2024
Viewed by 794
Abstract
Multiple sclerosis (MS) is one of the chronic and neurodegenerative diseases of the central nervous system (CNS). It generally affects motor, sensory, cerebellar, cognitive, and language functions. It is thought that identifying MS speech disorders using quantitative methods will make a significant contribution [...] Read more.
Multiple sclerosis (MS) is one of the chronic and neurodegenerative diseases of the central nervous system (CNS). It generally affects motor, sensory, cerebellar, cognitive, and language functions. It is thought that identifying MS speech disorders using quantitative methods will make a significant contribution to physicians in the diagnosis and follow-up of MS patients. In this study, it was aimed to investigate the speech disorders of MS via objective speech analysis techniques. The study was conducted on 20 patients diagnosed with MS according to McDonald’s 2017 criteria and 20 healthy volunteers without any speech or voice pathology. Speech data obtained from patients and healthy individuals were analyzed with the PRAAT speech analysis program, and classification algorithms were tested to determine the most effective classifier in separating specific speech features of MS disease. As a result of the study, the K-nearest neighbor algorithm (K-NN) was found to be the most successful classifier (95%) in distinguishing pathological sounds which were seen in MS patients from those in healthy individuals. The findings obtained in our study can be considered as preliminary data to determine the voice characteristics of MS patients. Full article
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