Evidence map›Paper›PMID 34012384›Full record

ArticleFrontiers in neuroscience2021

The Influence of the Type of Background Noise on Perceptual Learning of Speech in Noise.

Liping Zhang, Friederike Schlaghecken, James Harte, Katherine L Roberts

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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.

Liping ZhangDepartment of Otolaryngology-Head and Neck Surgery, Shandong Provincial ENT Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Friederike SchlagheckenDepartment of Psychology, University of Warwick, Coventry, United Kingdom.
James HarteWarwick Manufacturing Group, University of Warwick, Coventry, United Kingdom.
Katherine L RobertsDepartment of Psychology, University of Warwick, Coventry, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesAuditory perceptual learning studies tend to focus on the nature of the target stimuli. However, features of the background noise can also have a significant impact on the amount of benefit that participants obtain from training. This study explores whether perceptual learning of speech in background babble noise generalizes to other, real-life environmental background noises (car and rain), and if the benefits are sustained over time.

designNormal-hearing native English speakers were randomly assigned to a training (

resultsParticipants who received training had significantly higher post-test word identification accuracy than control participants for all three types of noise, although benefits were greatest for the babble noise condition and weaker for the car- and rain-noise conditions. Both training and control groups maintained their pre- to post-test improvement over a period of several weeks for speech in babble noise, but returned to pre-test accuracy for speech in car and rain noise.

conclusionThe findings show that training benefits can show some generalization from speech-in-babble noise to speech in other types of environmental noise. Both groups sustained their learning over a period of several weeks for speech-in-babble noise. As the control group received equal exposure to all three noise types, the sustained learning with babble noise, but not other noises, implies that a structural feature of babble noise was conducive to the sustained improvement. These findings emphasize the importance of considering the background noise as well as the target stimuli in auditory perceptual learning studies.

Indexed as

auditory trainingbabble noisegeneralizationperceptual learningspeech in noise

Identifiers

PMID34012384
PMCPMC8126633

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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.