Evidence map›Paper›PMID 39473980›Full record

ArticleProceedings of the ... International Congress of Phonetic Sciences. International Congress of Phonetic Sciences2019

Computer-Assisted Syllable Complexity Analysis of Continuous Speech as a Measure of Child Speech Disorders.

Marisha Speights Atkins, Suzanne E Boyce, Joel MacAuslan, Noah Silbert

Abstract read
In one paragraph

Article in Proceedings of the ... International Congress of Phonetic Sciences. International Congress of Phonetic Sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
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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

4 authors.

Marisha Speights AtkinsAuburn University.
Suzanne E BoyceUniversity of Cincinnati.
Joel MacAuslanSpeech Technology and Applied Research Corp.
Noah SilbertUniversity of Cincinnati.

Funding

Innovative Speech Articulation Tools for Neuroscience ResearchR44DC010104 · NIDCD · SPEECH TECHNOLOGY/APPLIED RESEARCH CORP. · PI BOYCE, SUZANNE ELIZABETH, FELL, HARRIET J · 2012 to 2014
$1.5M
NIDCD NIH HHS R44 DC010104
6 · The paper itself

Abstract

A common indicator of speech production disorders in children is a reduced ability to articulate complex syllables. Clinical studies of syllabic complexity of child speech have traditionally relied on phonetic transcription by trained listeners to characterize deviations in phonotatic structure. The labor-intensive nature of transcribing, segmenting, labeling, and hand-counting syllables has limited clinical analysis of large samples of continuous speech. In this paper, we discuss the use of a computer-assisted method, Automatic Syllabic Cluster Analysis, for broad transcription, segmentation, and counting syllabic units as a means for fast analysis of differences in speech precision when comparing children with and without speech-related disorders. Findings show that the number of syllabic clusters per utterance is a significant indicator of speech disorder.

Indexed as

automated analysischild speechcontinuous speechsyllables

Identifiers

PMID39473980
PMCPMC11520931

What OpenQuestion holds

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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.