Evidence map›Paper›PMID 40932333›Full record

ArticleJASA express letters2025

Can speech foundation models effectively identify languages in low-resource multilingual aging populations?

Aditya Kommineni, Rajat Hebbar, Sarah Petrosyan, Pranali Khobragade, Sudarsana Kadiri, Miguel Arce Rentería, Jinkook Lee, Shrikanth Narayanan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JASA express letters, 2025. 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. Review
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

8 authors.

Aditya KommineniMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, USA.ORCID 0009-0002-0056-8336
Rajat HebbarMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, USA.
Sarah PetrosyanDana and David Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California 90089, USA.
Pranali KhobragadeDana and David Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California 90089, USA.
Sudarsana KadiriMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, USA.ORCID 0000-0001-5806-3053
Miguel Arce RenteríaDepartment of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University College of Physicians and Surgeons, New York 10032, USAakommine@usc.edu, rajatheb@usc.edu, sarahritz23@gmail.com, pranalikhobragade@yahoo.com, skadiri@usc.edu, ma3347@cumc.columbia.edu, jinkookl@usc.edu, shri@usc.edu.
Jinkook LeeDana and David Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California 90089, USA.
Shrikanth NarayananMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, USA.

Funding

Leveraging a natural experiment to estimate the causal impact of blood sugar on dementia and cognition in IndiaR01AG051125 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Jinkook Lee · 2015 to 2026
$26.8M
Harmonized Diagnostic Assessment of Dementia (DAD) for Longitudinal Aging Study of India (LASI)-Genomic study.U01AG064948 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI KARDIA, SHARON L, LEE, JINKOOK · 2019 to 2023
$12.5M
Modifiable Protective and Risk Factors of Late-Life Cognitive Health and DementiaRF1AG055273 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI LANGA, KENNETH M, LEE, JINKOOK · 2017 to 2017
$4.0M
Multilingualism as a factor of resilience to Alzheimer's disease and related dementias in IndiaR01AG080473 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Leon Aksman, Miguel Arce · 2023 to 2026
$3.1M
NIA NIH HHS R01 AG051125NIA NIH HHS R01 AG080473NIA NIH HHS RF1 AG055273NIA NIH HHS U01 AG064948
6 · The paper itself

Abstract

Speech foundation models (SFMs) achieve state-of-the-art results in many tasks, but their performance on elderly, multilingual speech remains underexplored. In this work, we investigate SFMs' ability to analyze multilingual speech from older adults using spoken language identification as a proxy task. We propose three key qualities for foundation models to serve multilingual aging populations: robustness to input duration, invariance to speaker demographics, and few-shot transferability in low-resource settings. Zero-shot evaluation indicates a noticeable performance drop for shorter inputs. We find that native speakers' speech consistently outperforms non-native speech across languages. Few-shot learning indicates better transferability in larger models.

Indexed as

AgingLanguageMultilingualismSpeechAgedFemaleHumansMaleMiddle Aged

Identifiers

PMID40932333
PMCPMC12434620

What OpenQuestion holds

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LicenceCC BY-NC
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.