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Multi-talker speech comprehension at different temporal scales in listeners with normal and impaired hearing.

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  1. [1] § Results › HM-LSTM model performance ↔ code/hm_lstm/HierarchicalLM.py, lines 8 to 54 · score 0.51 · phoneme embeddings, HM LSTM, hidden, Hierarchical, predict, layers · Report a mismatch
  2. [2] § Materials and methods › Hierarchical multiscale LSTM model ↔ code/hm_lstm/layers.py, lines 7 to 61 · score 0.50 · lower layer, COPY, RNNs, cell, HM LSTM · Report a mismatch

Paper

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The authors' code

Python · 56 lines · 1.9 KB · no license · 1 match

  1. # standard LM stucture
  2. import torch
  3. # import pdb
  4. import torch.nn as nn
  5. import torch.nn.functional as F
  6. from layers import HM_LSTM
  8. class HLM(nn.Module):
  9. def __init__(self,dicts,phoneme_embedsize,hiddensizes):
  10. '''
  11. dicts: tuple(phonemedict,worddict,phrasedict)
  12. phoneme_embedsize: int
  13. hiddensizes: tuple(phoneme_hiddensize,syllable_hiddensize,word_hiddensize,phrase_hiddensize)
  14. '''
  15. super(HLM,self).__init__()
  16. self.phonemedict,self.worddict,self.phrasedict = dicts
  17. self.phoneme_embedsize = phoneme_embedsize
  18. _,_,_,self.phrase_hiddensize = hiddensizes
  20. #1. input layer (phoneme)：
  21. self.phonemeEmbed = nn.Embedding(len(self.phonemedict.tokenlist),self.phoneme_embedsize)
  23. #2. HM_LSTM:
  24. self.hlstm = HM_LSTM(self.phoneme_embedsize,hiddensizes)
  26. #3. output layer
  27. self.outlayer = nn.Linear(self.phrase_hiddensize*2,1)
  29. def forward(self,inputs,device):
  30. '''
  31. phonemesents tensor: batch * maxlen_p
  32. phrasesents tensor: batch * maxlen_ph
  33. phonemelengths list: batch
  34. phraselengths list: batch
  35. states tensor: batch * 3 * (maxlen_p-1)
  37. return predictions tensor: batchsize * maxlen_ph * phrasevoc
  38. '''
  39. phonemesents = inputs['phonemesents'].to(device)
  40. states = inputs['states'].to(device)
  42. #0. Initialization
  43. batchsize = phonemesents.shape[0]
  45. #1. embedding layer：
  46. phoneme_embeddings = self.phonemeEmbed(phonemesents) # batch * maxlen_p * embedsize
  48. #2. hlstm:
  49. phrase_hiddens = self.hlstm(phoneme_embeddings,states,device) # batchsize * (2 * hiddensize)
  51. #3. output:
  52. outputs = self.outlayer(phrase_hiddens) # batchsize * 1
  54. return outputs

HierarchicalLM.py, no license · at the source

Overview

Authors: Jixing Li1, Qixuan Wang2,3, Qian Zhou4, Lu Yang4, Yutong Shen5, Shujian Huang5, Shaonan Wang6, Liina Pylkkänen7, Zhiwu Huang4,8
ORCID iDs: Jixing Li, Qixuan Wang
  1. Department of Linguistics and Translation, City University of Hong Kong Hong Kong Hong Kong
  2. Department of Facial Plastic and Reconstructive Surgery, Eye and ENT Hospital, Fudan University Shanghai China
  3. ENT institute, Eye and ENT Hospital, Fudan University Shanghai China
  4. Department of Otolaryngology-Head and Neck Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine Shanghai China
  5. Department of Computer Science and Technology, Nanjing University Nanjing China
  6. Institute of Automation, Chinese Academy of Sciences Beijing China
  7. Department of Linguistics, Department of Psychology, New York University New York United States
  8. College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine Shanghai China
Institutions: City University of Hong Kong (Hong Kong SAR China); Fudan University (China); Shanghai Ninth People's Hospital (China); Nanjing University (China); Institute of Automation (China); New York University (United States); Shanghai Jiao Tong University (China)
Journal: eLife, volume 13, article RP100056
Dates: published online 26 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.100056 · PMID 42187018 · PMCID PMC13215671 · OpenAlex W4402129256
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: EEG (modality), human (organism), other condition (population), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Machine learning, Physiology & signal measures
Keywords: hierarchical linguistic units, hearing impairment, cocktail party, computational modeling, EEG, Human
MeSH: Comprehension*, Hearing Loss*, Speech Perception*, Adult, Electroencephalography, Female, Humans, Male, Middle Aged, Young Adult (* major topic)
Journal subjects: Neuroscience
Topic: Hearing Loss and Rehabilitation (Speech and Hearing, Health Professions), according to OpenAlex
Funding: City University of Hong Kong (7020086, 7200747); National Natural Science Foundation of China (82201273); Science and Technology Commission of Shanghai Municipality (22Y11902000); New York University Abu Dhabi (G1001)
Citations: not cited yet (Europe PMC); 49 references in the paper

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

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

OSF fjv5n

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: Python (9)
Size: 146 files, 9 scripts
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: PyTorch (5 files), NumPy (4 files), SciPy (3 files), MNE-Python (1 file), pandas (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
9 files
At the source: osf.io/fjv5n/

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.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 9 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

All code and preprocessed data are available at https://osf.io/fjv5n/. The raw data are not publicly shared because they contain potentially identifiable information and are subject to ethical restrictions. Interested researchers may request access to the original raw data by contacting the corresponding author at and submitting a project proposal. All requests will be reviewed by the Institutional Review Board at City University of Hong Kong. Use of the data for commercial research is not permitted.

The following dataset was generated:

LiJ Open Science Framework2026Multi-Talker Speech Comprehensionfjv5n

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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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://doi.org/10.7554/elife.100056

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/elife.100056},
url = {https://doi.org/10.7554/elife.100056},
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 - 2026/05/26
VL - 13
SP - RP100056
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.100056
UR - https://doi.org/10.7554/elife.100056
LA - en
ER -

CSL-JSON

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"title": "Multi-talker speech comprehension at different temporal scales in listeners with normal and impaired hearing",
"container-title": "eLife",
"author": [
{
"family": "Li",
"given": "Jixing"
},
{
"family": "Wang",
"given": "Qixuan"
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{
"family": "Zhou",
"given": "Qian"
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{
"family": "Yang",
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"family": "Shen",
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"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.100056",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
26
]
]
}
}

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