Multi-talker speech comprehension at different temporal scales in listeners with normal and impaired hearing.
The 2 matches
- [1] § Results › HM-LSTM model performance ↔ code/hm_lstm/HierarchicalLM.py, lines 8–54 · score 0.51 · phoneme embeddings, HM LSTM, hidden, Hierarchical, predict, layers
- [2] § Materials and methods › Hierarchical multiscale LSTM model ↔ code/hm_lstm/layers.py, lines 7–61 · score 0.50 · lower layer, COPY, RNNs, cell, HM LSTM
Paper
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The authors' code
Python · 56 lines · 1.9 KB · no license · 1 match
HierarchicalLM.py, no license · at the source
Overview
- Department of Linguistics and Translation, City University of Hong Kong Hong Kong Hong Kong
- Department of Facial Plastic and Reconstructive Surgery, Eye and ENT Hospital, Fudan University Shanghai China
- ENT institute, Eye and ENT Hospital, Fudan University Shanghai China
- Department of Otolaryngology-Head and Neck Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine Shanghai China
- Department of Computer Science and Technology, Nanjing University Nanjing China
- Institute of Automation, Chinese Academy of Sciences Beijing China
- Department of Linguistics, Department of Psychology, New York University New York United States
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine Shanghai China
Abstract
Comprehending speech requires deciphering a range of linguistic representations, from phonemes to narratives. Prior research suggests that in single-talker scenarios, the neural encoding of linguistic units follows a hierarchy of increasing temporal receptive windows. Shorter temporal units like phonemes and syllables are encoded by lower-level sensory brain regions, whereas longer units such as sentences and paragraphs are processed by higher-level perceptual and cognitive areas. However, the brain’s representation of these linguistic units under challenging listening conditions, such as a cocktail party situation, remains unclear. In this study, we recorded electroencephalogram (EEG) responses from both normal-hearing and hearing-impaired participants as they listened to individual and dual speakers narrating different parts of a story. The inclusion of hearing-impaired listeners allowed us to examine how hierarchically organized linguistic units in competing speech streams affect comprehension abilities. We leveraged a hierarchical language model to extract linguistic information at multiple levels—phoneme, syllable, word, phrase, and sentence—and aligned these model activations with the EEG data. Our findings showed distinct neural responses to dual-speaker speech between the two groups. Specifically, compared to normal-hearing listeners, hearing-impaired listeners exhibited poorer model fits at the acoustic, phoneme, and syllable levels, as well as the sentence levels, but not at the word and phrase levels. These results suggest that hearing-impaired listeners experience disruptions at both shorter and longer temporal scales, while their processing at medium temporal scales remains unaffected.
Reproduced under the paper's license (CC BY), from the paper cited above.
Repository
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OSF fjv5n
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
- 28 September 2026: the link answers (HTTP 200)
9 files, to read at the source
This repository has no license: its authors keep all rights. Read it at the source.
- code/
combine_ridge.py — Python, 38 lines, not shown here - code/
hm_lstm/ — Python, 56 lines, 1 match, not shown hereHierarchicalLM.py - code/
hm_lstm/ — Python, 134 lines, not shown heredataset.py - code/
hm_lstm/ — Python, 34 lines, not shown heredictionary.py - code/
hm_lstm/ — Python, 261 lines, 1 match, not shown herelayers.py - code/
hm_lstm/ — Python, 107 lines, not shown herepredict.py - code/
hm_lstm/ — Python, 236 lines, not shown hererun.py - code/
ridge_lstm.py — Python, 63 lines, not shown here - code/
stats.py — Python, 45 lines, not shown here
The paper's code and data availability statement is in the Data section.
Tracing map
Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.
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- 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data
No dataset and no data link were found in the paper.
Data availability
All code and preprocessed data are available at https://
The following dataset was generated:
LiJ Open Science Framework2026Multi-Talke
Reproduced under the paper's license (CC BY), from the paper cited above.
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Version 1, 28 September 2026: the first record
Recorded: type, language, journal, volume, pages, dates, 9 authors, 6 keywords, 10 MeSH terms, 4 funders, 48 references.
Cite
This paper
Li, J., Wang, Q., Zhou, Q., Yang, L., Shen, Y., Huang, S., Wang, S., Pylkkänen, L., & Huang, Z. (2026). Multi-talker speech comprehension at different temporal scales in listeners with normal and impaired hearing. eLife, 13, RP100056. https://
BibTeX
@article{li2026multi,
author = {Li, Jixing and Wang, Qixuan and Zhou, Qian and Yang, Lu and Shen, Yutong and Huang, Shujian and Wang, Shaonan and Pylkkänen, Liina and Huang, Zhiwu},
title = {{Multi-talker speech comprehension at different temporal scales in listeners with normal and impaired hearing}},
journal = {eLife},
year = {2026},
month = may,
volume = {13},
pages = {RP100056},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/
url = {https://
pmid = {42187018},
pmcid = {PMC13215671}
}
RIS
TY - JOUR
AU - Li, Jixing
AU - Wang, Qixuan
AU - Zhou, Qian
AU - Yang, Lu
AU - Shen, Yutong
AU - Huang, Shujian
AU - Wang, Shaonan
AU - Pylkkänen, Liina
AU - Huang, Zhiwu
TI - Multi-talker speech comprehension at different temporal scales in listeners with normal and impaired hearing
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/
VL - 13
SP - RP100056
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/
UR - https://
LA - en
ER -
CSL-JSON
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