Evidence map›Paper›PMID 34262851›Full record

ArticleJournal of second language pronunciation2018

Computer-Assisted Visual Articulation Feedback in L2 Pronunciation Instruction: A Review.

Heather Bliss, Jennifer Abel, Bryan Gick

Abstract read
In one paragraph

Article in Journal of second language pronunciation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. 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

3 authors.

Heather BlissUniversity of Victoria.
Jennifer AbelUniversity of British Columbia.
Bryan GickUniversity of British Columbia.

Funding

Making DeepEdge, a tool for ultrasound analysis, cloud-accessible.R01DC002717 · NIDCD · YALE UNIVERSITY · PI WHALEN, DOUGLAS H · 1996 to 2022
$12.8M
NIDCD NIH HHS R01 DC002717
6 · The paper itself

Abstract

Language learning is a multimodal endeavor; to improve their pronunciation in a new language, learners access not only auditory information about speech sounds and patterns, but also visual information about articulatory movements and processes. With the development of new technologies in computer-assisted pronunciation training (CAPT) come new possibilities for delivering feedback in both auditory and visual modalities. The present paper surveys the literature on computer-assisted visual articulation feedback, including direct feedback that provides visual models of articulation and indirect feedback that uses visualized acoustic information as a means to inform articulation instruction. Our focus is explicitly on segmental features rather than suprasegmental ones, with visual feedback conceived of as providing visualizations of articulatory configurations, movements, and processes. In addition to discussing types of visual articulation feedback, we also consider the criteria for effective delivery of feedback, and methods of evaluation.

Indexed as

articulationCAPTMultimodalitysegmental featuresvisual feedback

Identifiers

PMID34262851
PMCPMC8276941

What OpenQuestion holds

Textmetadata
LicenceTDM
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.