Degeneration of nerve cells that control cognitive, speech, and language processes leading to linguistic impairments at various levels, from verbal utterances to individual speech sounds, could indicate signs of neurological, cognitive and psychiatric disorders such as Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), dementias, depression, autism spectrum disorder, schizophrenia, etc. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. However, speech-based biomarkers could potentially offer many advantages over current clinical standards. In addition to being objective and naturalistic, they can also be collected remotely with minimal instruction and time requirements. Furthermore, Machine Learning algorithms developed to build automated diagnostic models using linguistic features extracted from speech could aid diagnosis of patients with probable diseases from a group of normal population.
To ensure that speech-based biomarkers are providing accurate measurement and can serve as effective clinical tools for detecting and monitoring disease, speech features extracted and analyzed must be systematically and rigorously evaluated. Different machine learning architectures trained to classify different types of disordered speech must also be rigorously tested and systematically compared.
For speech measures, three categories of evaluation have been proposed: verification, analytical validation, and clinical validation. Verification includes assessing and comparing the quality of speech recordings across hardware and recording conditions. Analytical validation entails checking the accuracy and reliability of data processing and computed measures to ensure that they are accurately measuring the intended phenomena. Clinical validity involves verifying the correspondence of a measure to clinical diagnosis, disease severity/progression, and/or response to treatment outcomes.
For machine learning algorithms, analytical and clinical validation apply. For example, the accuracy of different algorithms can be compared in different clinical groups for different outcome measures.
This Research Topic aims at bringing together research on the effectiveness of speech-based as biomarkers for the clinical diagnosis or the evaluation of disease severity and prognosis from related disciplines including cognitive neurosciences, computer sciences, engineering, linguistics, speech, communication sciences, etc. We welcome original research or systematic reviews on any of the three categories of evaluation of the speech measures: verification, analytical validation, clinical validation as well as NLP tools used to model clinical detection, classification and evaluation of disease severity/progression and/or response to treatment outcomes.
Topics may include, but are not limited to:
• Automatic analysis of dysarthric speech (e.g. typical and atypical Parkinsonism, Huntington's disease, Multiple Sclerosis, Amyotrophic Lateral Sclerosis);
• Early detection and classification of Neurodevelopmental Disorders (e.g. Autism Spectrum Disorder (ASD), Speech Language Impairment (SLI), Attention Deficit and Hyperactivity Disorder (ADHD));
• Cognitive assessment and clinical phenotypization of (e.g., Alzheimer's disease, cognitive Impairment including substance-induced cognitive impairment, dementia);
• Mental illness screening and diagnosis (e.g., Post-traumatic Stress Disorder (PTSD), Depressive Disorder, anxiety disorder, Bipolar Disorder, Schizophrenia);
• Novel methods and tools used to collect speech samples for the assessment of neurological, cognitive and psychiatric disorders.
Degeneration of nerve cells that control cognitive, speech, and language processes leading to linguistic impairments at various levels, from verbal utterances to individual speech sounds, could indicate signs of neurological, cognitive and psychiatric disorders such as Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), dementias, depression, autism spectrum disorder, schizophrenia, etc. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. However, speech-based biomarkers could potentially offer many advantages over current clinical standards. In addition to being objective and naturalistic, they can also be collected remotely with minimal instruction and time requirements. Furthermore, Machine Learning algorithms developed to build automated diagnostic models using linguistic features extracted from speech could aid diagnosis of patients with probable diseases from a group of normal population.
To ensure that speech-based biomarkers are providing accurate measurement and can serve as effective clinical tools for detecting and monitoring disease, speech features extracted and analyzed must be systematically and rigorously evaluated. Different machine learning architectures trained to classify different types of disordered speech must also be rigorously tested and systematically compared.
For speech measures, three categories of evaluation have been proposed: verification, analytical validation, and clinical validation. Verification includes assessing and comparing the quality of speech recordings across hardware and recording conditions. Analytical validation entails checking the accuracy and reliability of data processing and computed measures to ensure that they are accurately measuring the intended phenomena. Clinical validity involves verifying the correspondence of a measure to clinical diagnosis, disease severity/progression, and/or response to treatment outcomes.
For machine learning algorithms, analytical and clinical validation apply. For example, the accuracy of different algorithms can be compared in different clinical groups for different outcome measures.
This Research Topic aims at bringing together research on the effectiveness of speech-based as biomarkers for the clinical diagnosis or the evaluation of disease severity and prognosis from related disciplines including cognitive neurosciences, computer sciences, engineering, linguistics, speech, communication sciences, etc. We welcome original research or systematic reviews on any of the three categories of evaluation of the speech measures: verification, analytical validation, clinical validation as well as NLP tools used to model clinical detection, classification and evaluation of disease severity/progression and/or response to treatment outcomes.
Topics may include, but are not limited to:
• Automatic analysis of dysarthric speech (e.g. typical and atypical Parkinsonism, Huntington's disease, Multiple Sclerosis, Amyotrophic Lateral Sclerosis);
• Early detection and classification of Neurodevelopmental Disorders (e.g. Autism Spectrum Disorder (ASD), Speech Language Impairment (SLI), Attention Deficit and Hyperactivity Disorder (ADHD));
• Cognitive assessment and clinical phenotypization of (e.g., Alzheimer's disease, cognitive Impairment including substance-induced cognitive impairment, dementia);
• Mental illness screening and diagnosis (e.g., Post-traumatic Stress Disorder (PTSD), Depressive Disorder, anxiety disorder, Bipolar Disorder, Schizophrenia);
• Novel methods and tools used to collect speech samples for the assessment of neurological, cognitive and psychiatric disorders.