ArticleJournal of speech, language, and hearing research : JSLHR2021
A Comparison of Manual Versus Automated Quantitative Production Analysis of Connected Speech.
Article in Journal of speech, language, and hearing research : JSLHR, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed, 12 citations in OpenAlex.
- Differences in Connected Speech Outcomes Across Elicitation Methods.Aphasiology · 2024Article
- Automation of Language Sample Analysis.Journal of speech, language, and hearing research : JSLHR · 2023Article
- Anterior connectivity critical for recovery of connected speech after stroke.Brain communications · 2022Article
- A systematic review of studies on connected speech processing: Trends, key findings, and implications.Frontiers in psychology · 2022Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Purpose Analysis of connected speech in the field of adult neurogenic communication disorders is essential for research and clinical purposes, yet time and expertise are often cited as limiting factors. The purpose of this project was to create and evaluate an automated program to score and compute the measures from the Quantitative Production Analysis (QPA), an objective and systematic approach for measuring morphological and structural features of connected speech. Method The QPA was used to analyze transcripts of Cinderella stories from 109 individuals with acute-subacute left hemisphere stroke. Regression slopes and residuals were used to compare the results of manual scoring and automated scoring using the newly developed C-QPA command in CLAN, a set of programs for automatic analysis of language samples. Results The C-QPA command produced two spreadsheet outputs: an analysis spreadsheet with scores for each utterance in the language sample, and a summary spreadsheet with 18 score totals from the analysis spreadsheet and an additional 15 measures derived from those totals. Linear regression analysis revealed that 32 of the 33 measures had good agreement;
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