Evidence map›Paper›PMID 35171700›Full record

ArticleJournal of speech, language, and hearing research : JSLHR2022

Validating Automatic Diadochokinesis Analysis Methods Across Dysarthria Severity and Syllable Task in Amyotrophic Lateral Sclerosis.

Chelsea Tanchip, Diego L Guarin, Scotia McKinlay, Carolina Barnett, Sanjay Kalra, Angela Genge, Lawrence Korngut, Jordan R Green, James Berry, Lorne Zinman and 3 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of speech, language, and hearing research : JSLHR, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
4.2field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 15 citations in OpenAlex.

  1. A Cross-Language Study of Oral Diadochokinesis: Rates and Rhythm.Folia phoniatrica et logopaedica : official organ of the International Association of Logopedics and Phoniatrics (IALP) · 2026
    Article
  2. Differentiating upper- and lower motor neuron diseases using automated acoustic analysis.Amyotrophic lateral sclerosis & frontotemporal degeneration · 2025
    Article
  3. Article
  4. Review
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 2 institutions in 2 countries.

Chelsea TanchipDepartment of Speech-Language Pathology, Rehabilitation Sciences Institute, University of Toronto, Ontario, Canada.ORCID 0000-0002-7054-3209
Diego L GuarinDepartment of Biomedical Engineering, Florida Institute of Technology, Melbourne.
Scotia McKinlayDepartment of Speech-Language Pathology, Rehabilitation Sciences Institute, University of Toronto, Ontario, Canada.
Carolina BarnettDivision of Neurology, Department of Medicine, University of Toronto and University Health Network, Ontario, Canada.
Sanjay KalraNeuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada.
Angela GengeClinical Research Unit, Montreal Neurological Institute & Hospital, and Department of Neurology and Neurosurgery, McGill University, Québec, Canada.
Lawrence KorngutDepartment of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Alberta, Canada.
Jordan R GreenDepartment of Communication Sciences and Disorders, MGH Institute of Health Professions, Boston, MA.ORCID 0000-0002-1464-1373
James BerryDepartment of Neurology, Massachusetts General Hospital, Boston.
Lorne ZinmanDivision of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Ontario, Canada.ORCID 0000-0002-8096-8366
Azadeh YadollahiKITE, Toronto Rehabilitation Institute, University Health Network, Ontario, Canada.
Agessandro AbrahaoDivision of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Ontario, Canada.ORCID 0000-0003-1142-2842
Yana YunusovaDepartment of Speech-Language Pathology, Rehabilitation Sciences Institute, University of Toronto, Ontario, Canada.ORCID 0000-0002-2353-2275
Google (United States) · USFlorida Institute of Technology · US

Funding

The development and validation of a novel tool for the assessment of bulbar dysfunction in ALSR01DC017291 · NIDCD · SUNNYBROOK RESEARCH INSTITUTE · PI JORDAN R GREEN, Yana Yunusova · 2018 to 2026
$3.6M
SPEECH MOVEMENT CLASSIFICATION FOR ASSESSING AND TREATING ALSR01DC013547 · NIDCD · MGH INSTITUTE OF HEALTH PROFESSIONS · PI GREEN, JORDAN R · 2014 to 2018
$3.2M
Bulbar Motor Deterioration in ALSR01DC009890 · NIDCD · UNIVERSITY OF NEBRASKA LINCOLN · PI GREEN, JORDAN R, YUNUSOVA, YANA · 2009 to 2013
$2.3M
NIDCD NIH HHS R01 DC009890NIDCD NIH HHS R01 DC013547NIDCD NIH HHS R01 DC017291
6 · The paper itself

Abstract

purposeOral diadochokinesis (DDK) is a standard dysarthria assessment task. To extract automatic and semi-automatic DDK measurements, numerous DDK analysis algorithms based on acoustic signal processing are available, including amplitude based, spectral based, and hybrid. However, these algorithms have been predominantly validated in individuals with no perceptible to mild dysarthria. The behavior of these algorithms across dysarthria severity is largely unknown. Likewise, these algorithms have not been tested equally for various syllable types. The goal of this study was to evaluate the performance of five common DDK algorithms as a function of dysarthria severity, considering syllable types.

methodWe analyzed 282 DDK recordings of /ba/, /pa/, and /ta/ from 145 participants with amyotrophic lateral sclerosis. Recordings were stratified into mild, moderate, or severe dysarthria groups based on individual performance on the Speech Intelligibility Test. Analysis included manual and automatic estimation of the number of syllables, DDK rate, and cycle-to-cycle temporal variability (cTV). Validation metrics included Bland-Altman mixed-effects limits of agreement between manual and automatic syllable counts, recall and precision between manual and automatic syllable boundary detection, and Kendall's tau-b correlations between manual and algorithm-detected DDK rate and cTV.

resultsThe amplitude-based algorithm (absolute energy) yielded the strongest correlations with manual analysis across all severity groups for DDK rate (

conclusionsAlgorithms based on signal energy analysis appeared to be the most robust for DDK analysis across dysarthria severity and syllable types; however, it remains prone to error against severe dysarthria and alveolar syllable context. Further development is needed to address this important issue.

Indexed as

Amyotrophic Lateral SclerosisDysarthriaAcousticsAlgorithmsHumansSpeech Production Measurement

Identifiers

PMID35171700
PMCPMC9150739
OpenAlexW4213390597

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.