Evidence map›Paper›PMID 40945982›Full record

ArticleJournal of voice : official journal of the Voice Foundation2025

Toward Acoustic-Based Normalization of Laryngeal EMG for Improved Interspeaker Consistency in Muscle-to-Acoustic Mapping.

Josué D Martínez, Emiro J Ibarra, Jesús A Parra, Daryush D Mehta, James T Heaton, Robert E Hillman, Michal J Plocienniczak, Jameson C Cooper, Matías Zañartu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of voice : official journal of the Voice Foundation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Machine learning surrogate forward models for biomechanical laryngeal control.The Journal of the Acoustical Society of America · 2026
    Article
  2. Article
  3. Article
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

9 authors.

Josué D MartínezDepartment of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 23400003, Chile. Electronic address: josue.martinez@usm.cl.
Emiro J IbarraDepartment of Electronic Engineering and Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa Maria, Valparaiso 2390123, Chile.
Jesús A ParraDepartment of Electronic Engineering and Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa Maria, Valparaiso 2390123, Chile.
Daryush D MehtaCenter for Laryngeal Surgery and Voice Rehabilitation, Massachusetts General Hospital, Boston, MA, USA; Department of Surgery, Harvard Medical School, Boston, MA, USA.
James T HeatonCenter for Laryngeal Surgery and Voice Rehabilitation, Massachusetts General Hospital, Boston, MA, USA; Department of Surgery, Harvard Medical School, Boston, MA, USA.
Robert E HillmanCenter for Laryngeal Surgery and Voice Rehabilitation, Massachusetts General Hospital, Boston, MA, USA; Department of Surgery, Harvard Medical School, Boston, MA, USA.
Michal J PlocienniczakCenter for Laryngeal Surgery and Voice Rehabilitation, Massachusetts General Hospital, Boston, MA, USA; Department of Surgery, Harvard Medical School, Boston, MA, USA.
Jameson C CooperCenter for Laryngeal Surgery and Voice Rehabilitation, Massachusetts General Hospital, Boston, MA, USA.
Matías ZañartuDepartment of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 23400003, Chile.

Funding

Use of ambulatory biofeedback to improve behavioral treatment of vocal hyperfunctionP50DC015446 · NIDCD · MASSACHUSETTS GENERAL HOSPITAL · PI Jarrad Van Stan · 2017 to 2026
$25.5M
NIDCD NIH HHS P50 DC015446
6 · The paper itself

Abstract

Understanding how intrinsic laryngeal muscle activation relates to vocal output is essential for advancing experimental voice research. However, intrasubject variability remains a major challenge when comparing EMG recordings across sessions. To address this, a normalization framework based on acoustic targets was implemented to improve the consistency of EMG-acoustic relationships across sessions. Signals from the cricothyroid (CT), thyroarytenoid (TA), and lateral cricoarytenoid (LCA) muscles were collected across four sessions from a healthy adult performing a set of phonatory and non-phonatory vocal tasks. Muscle Activation Plots (MAPs) were generated to visualize the relationship between normalized EMG activity and acoustic outcomes, specifically fundamental frequency (f

Indexed as

Acoustic targetsFundamental frequencyIntramuscular electromyographyIntrasubject variabilityIntrinsic laryngeal musclesMuscle activation plotsNormalization

Identifiers

PMID40945982
PMCPMC12522205

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.