Evidence map›Paper›PMID 37003704›Full record

ArticleJASA express letters2023

Modeling the effect of linguistic predictability on speech intelligibility prediction.

Amin Edraki, Wai-Yip Chan, Daniel Fogerty, Jesper Jensen

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In one paragraph

Article in JASA express letters, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Amin EdrakiDepartment of Electrical and Computer Engineering, Queen's University, Kingston, Ontario K7L 3N6, Canada.
Wai-Yip ChanDepartment of Electrical and Computer Engineering, Queen's University, Kingston, Ontario K7L 3N6, Canada.
Daniel FogertyDepartment of Speech and Hearing Science, University of Illinois Urbana-Champaign, Champaign, Illinois 61820, USA.ORCID 0000-0002-2611-102X
Jesper JensenDemant A/S, Smørum 2765, Denmark a.edraki@queensu.ca, chan@queensu.ca, dfogerty@illinois.edu, jesj@demant.com.

Funding

Maximizing speech recognition under adverse listening conditionsR01DC015465 · NIDCD · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI FOGERTY, DANIEL · 2016 to 2020
$1.6M
NIDCD NIH HHS R01 DC015465
6 · The paper itself

Abstract

Many existing speech intelligibility prediction (SIP) algorithms can only account for acoustic factors affecting speech intelligibility and cannot predict intelligibility across corpora with different linguistic predictability. To address this, a linguistic component was added to five existing SIP algorithms by estimating linguistic corpus predictability using a pre-trained language model. The results showed improved SIP performance in terms of correlation and prediction error over a mixture of four datasets, each with a different English open-set corpus.

Indexed as

LinguisticsSpeech IntelligibilityAlgorithmsCognitionLanguage

Identifiers

PMID37003704
PMCPMC10026257

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read17
table measurements read2
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.