Evidence map›Paper›PMID 38312568›Full record

ArticleHeliyon2024

A standardised test to evaluate audio-visual speech intelligibility in French.

Loïc Le Rhun, Gerard Llorach, Tanguy Delmas, Clara Suied, Luc H Arnal, Diane S Lazard

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Loïc Le RhunInstitut Pasteur, Université Paris Cité, Inserm UA06, Institut de l'Audition, Paris, France.
Gerard LlorachAuditory Signal Processing, Dept. of Medical Physics and Acoustics, University of Oldenburg Oldenburg, Germany.
Tanguy DelmasInstitut Pasteur, Université Paris Cité, Inserm UA06, Institut de l'Audition, Paris, France.
Clara SuiedInstitut de Recherche Biomédicale des Armées, Département Neurosciences et Sciences Cognitives, Brétigny-sur-Orge, France.
Luc H ArnalInstitut Pasteur, Université Paris Cité, Inserm UA06, Institut de l'Audition, Paris, France.
Diane S LazardInstitut Pasteur, Université Paris Cité, Inserm UA06, Institut de l'Audition, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Lipreading, which plays a major role in the communication of the hearing impaired, lacked a French standardised tool. Our aim was to create and validate an audio-visual (AV) version of the French Matrix Sentence Test (FrMST). Design: Video recordings were created by dubbing the existing audio files. Sample: Thirty-five young, normal-hearing participants were tested in auditory and visual modalities alone (Ao, Vo) and in AV conditions, in quiet, noise, and open and closed-set response formats. Results: Lipreading ability (Vo) ranged from 1 % to 77%-word comprehension. The absolute AV benefit was 9.25 dB SPL in quiet and 4.6 dB SNR in noise. The response format did not influence the results in the AV noise condition, except during the training phase. Lipreading ability and AV benefit were significantly correlated. Conclusions: The French video material achieved similar AV benefits as those described in the literature for AV MST in other languages. For clinical purposes, we suggest targeting SRT80 to avoid ceiling effects, and performing two training lists in the AV condition in noise, followed by one AV list in noise, one Ao list in noise and one Vo list, in a randomised order, in open or close set-format.

Indexed as

Audio-visual interactionDubbingFrench matrix sentence testLipreadingSpeechreading

Identifiers

PMID38312568
PMCPMC10835303

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.