Evidence map›Paper›PMID 34256982›Full record

ArticleJournal of voice : official journal of the Voice Foundation2023

Analysis of COVID-19 Resulting Cough Using Formants and Automatic Speech Recognition System.

Ouissam Zealouk, Hassan Satori, Mohamed Hamidi, Naouar Laaidi, Amine Salek, Khalid Satori

Open access · bronzeAbstract read
In one paragraph

Article in Journal of voice : official journal of the Voice Foundation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
1.3field-weighted citation impact, top 19% of its field
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 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Cough biomarkers for diagnosis and monitoring of respiratory disease: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2026
    Pooled it
  2. Article
  3. COVID-19 assessment using HMM cough recognition system.International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management · 2023
    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

6 authors at 2 institutions in 1 country.

Ouissam ZealoukDepartment of Mathematics and Computer Science, Faculty of Sciences Dhar Mahraz, Sidi Mohammed Ben Abbdallah University, Fez, Morocco. Electronic address: Ouissam.zealouk@gmail.com.
Hassan SatoriDepartment of Mathematics and Computer Science, Faculty of Sciences Dhar Mahraz, Sidi Mohammed Ben Abbdallah University, Fez, Morocco.
Mohamed HamidiDepartment of Mathematics and Computer Science, Faculty of Sciences Dhar Mahraz, Sidi Mohammed Ben Abbdallah University, Fez, Morocco.
Naouar LaaidiDepartment of Mathematics and Computer Science, Faculty of Sciences Dhar Mahraz, Sidi Mohammed Ben Abbdallah University, Fez, Morocco.
Amine SalekFaculty of Medicine and Pharmacy, UMP, Oujda, Morocco.
Khalid SatoriDepartment of Mathematics and Computer Science, Faculty of Sciences Dhar Mahraz, Sidi Mohammed Ben Abbdallah University, Fez, Morocco.
Sidi Mohamed Ben Abdellah University · MAMohamed I University · MA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As part of our contributions to researches on the ongoing COVID-19 pandemic worldwide, we have studied the cough changes to the infected people based on the Hidden Markov Model (HMM) speech recognition classification, formants frequency and pitch analysis. In this paper, An HMM-based cough recognition system was implemented with 5 HMM states, 8 Gaussian Mixture Distributions (GMMs) and 13 dimensions of the basic Mel-Frequency Cepstral Coefficients (MFCC) with 39 dimensions of the overall feature vector. A comparison between formants frequency and pitch extracted values is realized based on the cough of COVID-19 infected people and healthy ones to confirm our cough recognition system results. The experimental results present that the difference between the recognition rates of infected and non-infected people is 6.7%. Whereas, the formant analysis variation based on the cough of infected and non-infected people is clearly observed with F1, F3, and F4 and lower for F0 and F2.

Indexed as

COVID-19Speech Recognition SoftwareCoughHumansPandemicsSpeechCOVID-19—Cough recognition—Formants—HMM—ASR—Pitch

Identifiers

PMID34256982
PMCPMC8205259
OpenAlexW3168678459

What OpenQuestion holds

Textmetadata
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.