Evidence map›Paper›PMID 30464431›Full record

ArticleClinical interventions in aging2018

Categorization of sentence recognition for older adults under noisy and time-altered conditions.

Saea Kim, Sunmi Ma, Jihyeon Lee, Woojae Han

Abstract read
In one paragraph

Article in Clinical interventions in aging, 2018. 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
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Saea KimLaboratory of Hearing and Technology, Division of Speech Pathology and Audiology, College of Natural Science, Hallym University, Chuncheon, Korea, woojaehan@hallym.ac.kr.
Sunmi MaLaboratory of Hearing and Technology, Division of Speech Pathology and Audiology, College of Natural Science, Hallym University, Chuncheon, Korea, woojaehan@hallym.ac.kr.
Jihyeon LeeLaboratory of Hearing and Technology, Division of Speech Pathology and Audiology, College of Natural Science, Hallym University, Chuncheon, Korea, woojaehan@hallym.ac.kr.
Woojae HanLaboratory of Hearing and Technology, Division of Speech Pathology and Audiology, College of Natural Science, Hallym University, Chuncheon, Korea, woojaehan@hallym.ac.kr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeWhile evaluating the speech recognition ability of older adults, the present study aimed to analyze their error types in parts of speech and find error patterns under various conditions of background noise level and speed of speech.

methodsTwenty older adults with normal hearing for their age (NHiA) and 20 older adults with sensorineural hearing loss (SNHL) participated. Their cognitive function was screened as within the normal range (mini-mental state examination scores >25). The SNHL listeners were divided into high performers (SNHL-H; n=12) and low performers (SNHL-L; n=8), based on their achieving word recognition scores above or below 70%, respectively. A sentence recognition test was conducted at four levels of signal-to-noise ratio (SNR; eg, no noise, +6, +3, 0 dB) and four conditions of time alteration (eg, 30% and 15% of compression and expansion) at the most comfortable level for each participant.

resultsAs expected, the three groups showed that the error percentage increased in sentence recognition as either the SNR decreased or the speech rate became faster. Interestingly, a larger performance difference was found between the SNHL-H and SNHL-L groups in the condition of time alteration than in that of background noise. Among the parts of speech, nouns presented the highest error scores for all participants regardless of degree of listening difficulty. The noun errors of the three groups mainly consisted of no response and fail patterns, but substitution and omission were identified as the third pattern of noun error for background noise and fast speech, respectively.

conclusionDeterioration of speech recognition from the hearing threshold and supra-threshold auditory processing was seen in the elderly in difficult listening environments such as background noise and time alteration. Although different group performance ran across the eight experimental conditions, the robustness of noun errors and the error patterns were very similar, which might be extended to a possible clinical application of aural rehabilitation for the elderly population.

Indexed as

Acoustic StimulationRecognition, PsychologySpeech PerceptionTime PerceptionAgedAuditory ThresholdFemaleHearing Loss, SensorineuralHumansMaleNoiseSpeech Reception Threshold Testage-related perceptual errordistracting listening conditionerror patternnoun errorsentence perception

Identifiers

PMID30464431
PMCPMC6219098

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LicenceCC BY-NC
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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.