Evidence map›Paper›PMID 42403968›Full record

ArticleJournal of the American Academy of Audiology2026

Bilingualism and Aging: Their Impact on Speech Perception in Noise Using the AzBio Sentence Test.

Miwako Hisagi, Beatriz Barragan, Karina Romero, Margaret Winter

Abstract read
In one paragraph

Article in Journal of the American Academy of Audiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Miwako HisagiDepartment of Communication Disorders, Doctor of Audiology Program, California State University, Los Angeles, Los Angeles, CA.ORCID https://orcid.org/0000-0001-6788-9041
Beatriz BarraganDepartment of Speech-Language Pathology, AT Still University, Mesa, AZ.
Karina RomeroDepartment of Communication Disorders, Doctor of Audiology Program, California State University, Los Angeles, Los Angeles, CA.
Margaret WinterDepartment of Communication Disorders, Doctor of Audiology Program, California State University, Los Angeles, Los Angeles, CA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: With age-related hearing loss on the rise and a large bilingual (BI) population in the United States, there is an increasing need for effective assessment tools for Spanish-speaking and BI older adults. Research in this area is limited. The AzBio Sentence Test (AzBio) is used to assess speech perception in challenging listening environments. A Spanish version was recently developed to address the needs of Spanish-speaking patients, and opens new opportunities for research with BI individuals to better understand speech perception processes in both the native and the second language. Purpose: This preliminary study aims to explore the characteristics of speech recognition among older BI adults by examining the performance of younger and older proficient Spanish-English BIs on the AzBio in quiet and noisy conditions. Research Design: Preliminary quasi-experimental group design. Study Sample: Twenty-one participants were divided into three groups: eight young American English monolinguals (M = 28.12 years, range: 24-32), eight young Spanish-English BIs (M = 28.38 years, range: 23-33), and five older Spanish-English BIs (M = 58 years, range: 55-62). Data Collection and Analysis: The AzBio was used to assess speech perception in quiet and noisy conditions in both English and Spanish, with two signal-to-noise ratios: 0 and -3 dB. Independent and paired sample statistical tests were conducted. Results: In quiet conditions, older BIs outperformed younger ones on the Spanish version, whereas younger BIs performed better in English. However, for the older group, the linguistic advantage observed in quiet conditions disappeared in noisy environments, because performance differences with the younger group diminished when background noise was introduced. Young monolinguals and BIs showed no difference in performance on the English version. Conclusions: Aging poses challenges for speech recognition in noisy environments. For BIs, these difficulties extend to both languages, including the dominant one. The combined effects of bilingualism and aging negatively impact speech recognition in both languages. Clinical Relevance Statement: Understanding how individuals perform on the AzBio and other BI sentence recognition tools is essential for improving clinical assessment and intervention for those impacted by language background and age-related hearing changes.

Indexed as

AgingMultilingualismNoiseSpeech PerceptionAdultAgedFemaleHumansMaleYoung Adultagingbilingualismspeech recognition in noise

Identifiers

PMID42403968
PMCPMC13327959

What OpenQuestion holds

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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.