Evidence map›Paper›PMID 41358598›Full record

ArticleTrends in hearing

Voice Familiarization Training Improves Speech Intelligibility and Reduces Listening Effort.

Freja Baxter, Harriet J Smith, Emma Holmes

Abstract read
In one paragraph

Article in Trends in hearing. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

What it found

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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.

2 · The registry

The trial behind it

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Freja BaxterDivision of Psychology and Language Sciences, Department of Speech Hearing and Phonetic Sciences, University College London, London, UK.
Harriet J SmithDivision of Psychology and Language Sciences, Department of Speech Hearing and Phonetic Sciences, University College London, London, UK.ORCID 0000-0003-4314-6571
Emma HolmesDivision of Psychology and Language Sciences, Department of Speech Hearing and Phonetic Sciences, University College London, London, UK.ORCID 0000-0002-0314-6588

Funding

Wellcome Trust
6 · The paper itself

Abstract

Understanding speech among competing speech poses a substantial challenge. In these environments, familiar voices-including naturally familiar (e.g., friends, partners) and lab-trained voices-are more intelligible than unfamiliar voices. Yet, whether familiar voices also require less effort to understand is currently unknown. We trained 20 participants to become familiar with three voices, then tested listening effort during a speech intelligibility task. During familiarization and training, participants were exposed to three talkers for different lengths of time, either speaking 88, 166, or 478 sentences ("Least Familiar," "Moderately Familiar," or "Most Familiar" voice, respectively). During each trial of the speech intelligibility task, two competing sentences were presented at a target-to-masker ratio (TMR) of -6 or +3 dB. Participants reported target sentences that were spoken by trained or by novel, unfamiliar talkers. We assessed effort using self-reported ratings and physiologically, using pupil dilation. We found that self-report scores were more sensitive than pupil dilation to differences in TMR, with lower self-reported effort at +3 than -6 dB TMR. The two measures may also be differentially sensitive to the extent of training. We found lower self-reported effort for all three trained voices over unfamiliar voices, with no differences among the trained voices, whereas pupil dilation was only lower for the voice that had been trained for the longest. Thus, both self-report scores and pupil dilation showed advantages for the voice that was trained for the longest (∼1 h), but self-report scores additionally showed reduced effort even following relatively short durations of training (<10 min).

Indexed as

Perceptual MaskingRecognition, PsychologySpeech IntelligibilitySpeech PerceptionVoiceAcoustic StimulationAdultComprehensionFemaleHumansMaleNoisePupilSpeech AcousticsYoung Adultcocktail party listeningeffortpupillometryspeaker familiarityspeech perceptionvoice familiarity

Identifiers

PMID41358598
PMCPMC12686366

What OpenQuestion holds

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LicenceCC BY
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.