Acoustic analysis of voice in bulbar amyotrophic lateral sclerosis: a systematic review and meta-analysis of studies

Chiaramonte, Rita; Bonfiglio, Marco

doi:10.1080/14015439.2019.1687748

Cited by 33 publications

(32 citation statements)

References 61 publications

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“…Both instruments, however, lack sensitivity to early bulbar changes 9 . Several studies have found that speech features, such as jitter, shimmer, articulatory rate, speaking rate, and pause rate, are affected in ALS 10,11 , and that these can be measured from remotely-collected speech samples 12,13 . However, no study has assessed the sensitivity of remote speech analysis in detecting and tracking bulbar change.…”

Section: Introductionmentioning

confidence: 99%

Early detection and tracking of bulbar changes in ALS via frequent and remote speech analysis

et al. 2020

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Bulbar deterioration in amyotrophic lateral sclerosis (ALS) is a devastating characteristic that impairs patients’ ability to communicate, and is linked to shorter survival. The existing clinical instruments for assessing bulbar function lack sensitivity to early changes. In this paper, using a cohort of N = 65 ALS patients who provided regular speech samples for 3–9 months, we demonstrated that it is possible to remotely detect early speech changes and track speech progression in ALS via automated algorithmic assessment of speech collected digitally.

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Section: Introductionmentioning

confidence: 99%

Early detection and tracking of bulbar changes in ALS via frequent and remote speech analysis

et al. 2020

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“…Given that there was a large number of features, we selected features that have been used in papers related to clinical speech analytics and features that readers would be familiar with. For example, jitter and shimmer aim to measure instability in the voice, which has been found to become pronounced in ALS and Parkinson disease patients [17][18][19]. The second MFCC coefficient has been used to classify between patients with Parkinson disease and healthy controls [20].…”

Section: Summary Of Resultsmentioning

confidence: 99%

Repeatability of Commonly Used Speech and Language Features for Clinical Applications

et al. 2020

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Introduction: Changes in speech have the potential to provide important information on the diagnosis and progression of various neurological diseases. Many researchers have relied on open-source speech features to develop algorithms for measuring speech changes in clinical populations as they are convenient and easy to use. However, the repeatability of open-source features in the context of neurological diseases has not been studied. Methods: We used a longitudinal sample of healthy controls, individuals with amyotrophic lateral sclerosis, and individuals with suspected frontotemporal dementia, and we evaluated the repeatability of acoustic and language features separately on these 3 data sets. Results: Repeatability was evaluated using intraclass correlation (ICC) and the within-subjects coefficient of variation (WSCV). In 3 sets of tasks, the median ICC were between 0.02 and 0.55, and the median WSCV were between 29 and 79%. Conclusion: Our results demonstrate that the repeatability of speech features extracted using open-source tool kits is low. Researchers should exercise caution when developing digital health models with open-source speech features. We provide a detailed summary of feature-by-feature repeatability results (ICC, WSCV, SE of measurement, limits of agreement for WSCV, and minimal detectable change) in the online supplementary material so that researchers may incorporate repeatability information into the models they develop.

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“…Acoustic parameters and intelligibility tests could be used to develop a quantitative metric of severity in speech disorders related to several neurological disorders 56,57 . These parameters could guide rehabilitation to improve the speech intelligibility, help to predict the prognosis, and determine the side of the lesion 58 .…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review of Measures of Dysarthria Severity in Stroke Patients

Chiaramonte

Vecchio

2020

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Objective To define methods to measure dysarthria due to stroke and guide physicians in delineating a diagnostic protocol using the best current strategies. Design Systematic review. Literature Survey A search was conducted on PubMed, EMBASE, the Cochrane Library, and Web of Science to identify measurement methods for dysarthria severity in adults after stroke. Methods Two reviewers independently reviewed articles and came to a consensus about which ones to include. The authors excluded all duplicates, articles involving individuals with aphasia or other speech problems other than dysarthria, and articles unrelated to stroke. Articles were included if diagnostic measures were used to examine the effectiveness of speech rehabilitation in stroke patients. Synthesis The search identified 1154 articles with the keywords “stroke” OR “ictus” OR “cerebral vascular accident” AND “dysarthria” OR “Speech and Language Disorders” AND “diagnosis” OR “assessment.” The reviewers analyzed 86 full texts. There were 37 publications that met the criteria and were included in the systematic review. These articles were used to describe the main methods used for measuring the severity of stroke‐related dysarthria before and after speech rehabilitation. Conclusion Despite the range of diagnostic tools available, robust trials are lacking, and the diagnostic approaches are always different. More research is needed to find the best diagnostic methodologies and delineate a definitive diagnostic protocol.

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Acoustic analysis of voice in bulbar amyotrophic lateral sclerosis: a systematic review and meta-analysis of studies

Cited by 33 publications

References 61 publications

Early detection and tracking of bulbar changes in ALS via frequent and remote speech analysis

Early detection and tracking of bulbar changes in ALS via frequent and remote speech analysis

Repeatability of Commonly Used Speech and Language Features for Clinical Applications

A Systematic Review of Measures of Dysarthria Severity in Stroke Patients

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