Evidence map›Paper›PMID 37672785›Full record

ArticleJournal of speech, language, and hearing research : JSLHR2023

The Korean Speech Recognition Sentences: A Large Corpus for Evaluating Semantic Context and Language Experience in Speech Perception.

Jieun Song, Byungjun Kim, Minjeong Kim, Paul Iverson

Open access · hybridAbstract read
In one paragraph

Article in Journal of speech, language, and hearing research : JSLHR, 2023. 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
0.3field-weighted citation impact, top 40% of its field
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

0 citing papers in PubMed, 1 citations in OpenAlex.

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

4 authors at 2 institutions in 2 countries.

Jieun SongSchool of Digital Humanities and Computational Social Sciences, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.ORCID 0000-0003-3223-2124
Byungjun KimCenter for Digital Humanities and Computational Social Sciences, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
Minjeong KimGraduate School of Culture Technology, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
Paul IversonDepartment of Speech, Hearing and Phonetic Sciences, University College London, United Kingdom.
Korea Advanced Institute of Science and Technology · KRUniversity College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe aim of this study was to develop and validate a large Korean sentence set with varying degrees of semantic predictability that can be used for testing speech recognition and lexical processing.

methodSentences differing in the degree of final-word predictability (predictable, neutral, and anomalous) were created with words selected to be suitable for both native and nonnative speakers of Korean. Semantic predictability was evaluated through a series of cloze tests in which native (

resultsThe results of the speech-in-noise experiment demonstrated that the intelligibility of the sentences was similar to that of related English corpora. That is, intelligibility was significantly different depending on the semantic condition, and the sentences had the right degree of difficulty for assessing intelligibility differences depending on noise levels and language experience.

conclusionsThis corpus (1,021 sentences in total) adds to the target languages available in speech research and will allow researchers to investigate a range of issues in speech perception in Korean. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.24045582.

Indexed as

SemanticsSpeech PerceptionHumansLanguageRepublic of KoreaSpeech

Identifiers

PMID37672785
PMCPMC10558151
OpenAlexW4386468664

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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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.